The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions
Abstract
This is not another framework or productivity analysis. SAIER Volume 7 is a snapshot from our vantage point, bringing together practitioners, researchers, advocates, and communities grappling with AI's realities on the ground. It captures perspectives from Canada, the US, Europe, Asia, and Africa, though we recognize many voices remain absent. We hope it generates more questions than it answers and encourages people from all walks of life to ask hard questions and be critical. It also reflects our entrepreneurial roots at the Montreal AI Ethics Institute (MAIEI), less interested in abstract principles and more focused on what's actually happening in the field. What does implementation look like? What's working? What's failing? Where are the gaps between policy rhetoric and lived experience? We ground our work in understanding AI as sociotechnical systems. This means recognizing the technical components (software, hardware, data centres, chips), the social dimensions (people, culture, values, organizations), and the environmental impacts. Ignoring these interconnected realities reduces AI governance to compliance checklists when the real challenges are about trust, power, and lived impacts. We're also humble enough to know we're not capturing everything. Regions remain underrepresented. This isn’t the definitive state of AI ethics. It’s a state of AI ethics, from where we sit, in 2025. AI ethics, fundamentally, is a process and not a destination. This volume is our contribution to this ongoing conversation.
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Montreal AI Ethics Institute November 2025 STATE OF AI ETHICS REPORT | VOLUME 7 AI at the Crossroads: A Practitioner's Guide to Community-Centered Solutions Editors: Renjie Butalid Connor Wright Ismael Kherroubi García DOI: 10.5281/zenodo.17328882
State of AI Ethics Report | Volume 7 November 2025 2025 Montreal AI Ethics Institute (MAIEI) CC BY 4.0 This work is licensed under the Creative Commons Attribution 4.0 International License, ensuring broad accessibility while respecting contributor rights. To view a copy of this license, please visit http://creativecommons.org/licenses/by/4.0. Front & Back Cover Image: Distorted Lake Trees by Lone Thomasky & Bits&Bäume / Better Images of AI / CC BY 4.0. ALT text: A bird's eye view photo of a small yellow aeroplane flying over a river or lake, interspersed with trees and clouds. However, the image is slightly distorted with digital artifacts. DOI: 10.5281/zenodo.17328882 1
State of AI Ethics Report | Volume 7 November 2025 Dedicated to Abhishek Gupta Founder & Principal Researcher Montreal AI Ethics Institute In Memoriam (Dec 20, 1992 – Sep 30, 2024) DOI: 10.5281/zenodo.17328882 2
State of AI Ethics Report | Volume 7 November 2025 Published by the Montreal AI Ethics Institute (MAIEI) Montreal, Quebec, Canada November 4, 2025 The State of AI Ethics Report (Volume 7) DOI: 10.5281/zenodo.17328882 Suggested Citation For citing the entire report: Butalid, R., Wright, C. & Kherroubi García, I. (Eds.). (2025). The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide to Community-Centered Solutions. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. For citing individual pieces within the report: [Author Surname], [Initials]. (2025). [Title of piece]. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. [xx-xx]. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. Disclaimer: The views and opinions expressed in this report are those of the individual contributors and do not necessarily reflect the official policy or position of the Montreal AI Ethics Institute (MAIEI) or its affiliated organizations. The information contained in this report is for general informational purposes and should not be construed as professional advice. While every effort has been made to ensure accuracy, MAIEI makes no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, or suitability of the information contained herein. Contact Information Website: https://montrealethics.ai Email: [email protected] DOI: 10.5281/zenodo.17328882 3
State of AI Ethics Report | Volume 7 November 2025 Contributors This year's State of AI Ethics Report (SAIER) Volume 7 represents the collective expertise and insights of researchers, practitioners, policymakers, industry advocates, MAIEI collaborators and advisors from around the world. We are deeply grateful to each contributor who shared their knowledge, analysis, and perspectives to make this report a comprehensive resource for the AI ethics community. Your contributions help ensure that conversations about AI ethics remain grounded, inclusive, and forward-looking. Thank you for lending your voice to this important work. Anna Sikorski ACTRA Montreal Kent Sikstrom ACTRA National Shi Kang’ethe AIVERSE Trisha Ray Atlantic Council Linda Solomon Wood Canada’s National Observer Rosa E. Martín Peña Centre for Ethics and Law in the Life Sciences (CELLS), Leibniz University Hannover Kirthi Jayakumar civitatem resolutions Aimee Li, Anna Zhou, Chelsea Sun, Kanika Singh Pundir, Roberto Concepcion, Rose Simon, & Tao Liu Encode Canada Michelle Baldwin Equity Cubed Katrina Ingram Ethically Aligned AI Jae-Seong Lee Electronics and Telecommunications Research Institute (ETRI), South Korea Denise Williams First Nations Technology Council (former CEO) David Atkinson Georgetown University Rachel Adams, Global Center on AI Governance; Leverhulme Centre for the Future of Intelligence, University of Cambridge Daniel S. Schiff Governance and Responsible AI Lab (GRAIL), Purdue University Jennifer Laplante Government of Nova Scotia, Canada Joahna Kuiper HiirAI Kate Arthur Independent, Author Jake Wildman-Sisk Independent, Lawyer Jonathan van Geuns Independent Researcher Priscila Chaves Martínez Independent Researcher Amanda Silvera Independent, Voice Actor Wan Sie Lee Infocomm Media Development Authority of Singapore Ismael Kherroubi Garcia (Editor) Kairoi; The Responsible Artificial Intelligence Network (RAIN); MAIEI Tariq Khan London Borough of Camden County Council, United Kingdom Connor Wright (Editor) MAIEI Renjie Butalid (Editor) MAIEI Marianna Ganapini MAIEI; University of North Carolina at Charlotte DOI: 10.5281/zenodo.17328882 4
State of AI Ethics Report | Volume 7 November 2025 Ana Brandusescu McGill University Renée Sieber McGill University Jimmy Y. Huang McGill University; MAIEI Elizabeth M. Adams Minnesota Responsible AI Institute Seher Shafiq Mozilla Foundation Kathy Baxter Salesforce Ivy Seow Singapore Management University Tamas Makanay Singapore Management University Burkhard Mausberg Small Change Fund Shay Kennedy Small Change Fund Alex Tveit Sustainable Impact Foundation Bryan Lozano Tech:NYC Foundation Jenni Warren Tech:NYC Foundation Adnan Akbar tekniti.ai Ayaz Syed The Dais, Toronto Metropolitan University Blair Attard-Frost University of Alberta; Alberta Machine Intelligence Institute Eliot Tretter University of Calgary Fabio Tollon University of Edinburgh Roxana Akhmetova University of Oxford Zoya Yasmine University of Oxford Jess Reia University of Virginia Maria Lungu University of Virginia Ryan Burns University of Washington Bothell Tania Duarte We and AI DOI: 10.5281/zenodo.17328882 5
State of AI Ethics Report | Volume 7 November 2025 Acknowledgments Thank you to our Paid Subscribers who make it possible to keep The AI Ethics Brief free and accessible to everyone. Your support sustains our work in democratizing AI ethics literacy and honouring Abhishek Gupta’s legacy. You are part of the SAIER Champions Circle 2025. SAIER Vol. 7 Team: ● Editors: Renjie Butalid, Connor Wright, Ismael Kherroubi Garcia ● Copy Editor & Marketing: Kei Baritugo ● Digital Production: Zahra Mustin Many thanks to the MAIEI team members and extended community of supporters and advisors: Masa Sweidan, Megan Tan, Hannah McGee, Sadia Rafiquddin, Mo Akif, Meriem Mehri, Paolina Buck, Justin Hendrix, Rebecca Finlay, Steve Rennie, Ilias Benjelloun, Simran Kanda, Marc-Antoine Bonin, Liandra Doonan, Karen Yum, Vinod Rajasekaran, Nishan Chelvachandran, Luca Baraldi, Laura Zambarda, Tom Sinclair, and the entire team behind the Wadham Experience, Wadham College, Oxford. Your dedication, collaboration, and ongoing support made this report possible and continue to strengthen MAIEI's impact. Special mention goes to Abhishek’s family: his parents, Ashok and Asha, and brother, Abhijay Gupta. Your continued connection to MAIEI's mission means the world to us. DOI: 10.5281/zenodo.17328882 6
State of AI Ethics Report | Volume 7 November 2025 Table of Contents INTRODUCTION TO SAIER VOLUME 7 10 0.1 Opening Foreword - State of AI Ethics Report Vol. 7 11 0.2 Philosophy, AI Ethics and Practical Implementations 15 0.3 Bridging Policy and Ethics: On the Launch of AI Policy Corner 17 PART I: FOUNDATIONS & GOVERNANCE 20 Chapter 1: Global AI Governance at the Crossroads 21 1.1 Competing AI Action Plans: Regional Bloc Responses to the US and China 22 1.2 What "AI Sovereignty" Means for Nations Without Superpower Resources 25 Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics 28 2.1 The Institutions Behind the Concepts 29 2.2 The Contested Meanings of Responsible AI 32 2.3 The Evolving AI Safety Conversation: Singapore’s Practical Path Forward 35 Chapter 3: From Principles to Practice – Implementing AI Ethics in Organizations 38 3.1 AI Governance in Practice: 2025 Trends in Understanding and Implementation 39 3.2 Monetization and Closing the Principles-to-Practice Gap 41 3.3 From Solidarity to Practice: Building Ethical AI Capacity in Africa 43 PART II: SOCIAL JUSTICE & EQUITY 46 Chapter 4: Democracy and AI Disinformation 47 4.1 Legislating the Moving Digital Terrain 48 4.2 AI and the Body Politic 51 4.3 Reinforcing the Feedback Loop: How AI in Elections Deepens Democratic Inequities 54 Chapter 5: Algorithmic Justice in Practice 57 5.1 Algorithmic Justice vs. State Power 58 5.2 AI Ethics and Gender Diversity in the US: From Surveillance to Resistance 60 5.3 Beyond the Algorithm: Why Student Success is a Sociotechnical Challenge 62 Chapter 6: AI Surveillance, Privacy, and Human Rights 65 6.1 AI, Surveillance, and the Public Good 66 6.2 Challenging Mandated AI in the Public Sector 69 6.3 AI, Biometrics, and Canada’s Developing Legal Framework in 2025 71 DOI: 10.5281/zenodo.17328882 7
State of AI Ethics Report | Volume 7 November 2025 Chapter 7: Environmental Impact of AI 74 7.1 The Subtle and Not-so-subtle Environmental Impacts of AI 75 7.2 Measuring the Environmental Impact of the AI Supply Chain 78 7.3 Policies Centring AI’s Resource Consumption 80 PART III: SECTORAL APPLICATIONS 83 Chapter 8: Healthcare AI – When Algorithms Meet Patient Care 84 8.1 Learning to Diagnose: How AI’s Digital Twins Are Redefining Patient Care 85 8.2 Medical Trade Unions and Professional Bodies are Taking Back Control and Oversight of AI in Healthcare 87 Chapter 9: AI in Education – Tools, Policies, and Institutional Change 89 9.1 Building Confidence for Class Participation 90 9.2 Generative AI at Universities: Accounts from the Front-Line 92 Chapter 10: AI and Labour Justice 95 10.1 Restoring Employee Trust in AI 96 10.2 AI in Oil and Gas: The Case of Alberta 98 Chapter 11: AI in Arts, Culture, and Media 100 11.1 Media Jobs are Canaries in the AI Automation Coal Mine 101 11.2 2025 Marks a New Era for Canadian Performers: The First Collective Agreements with AI Protections 104 11.3 The Ursula Exchange 107 PART IV: EMERGING TECHNOLOGIES 110 Chapter 12: Military AI and Autonomous Weapons 111 12.1 A Minute Before Escalation: Algorithmic Power and the New Military-Industrial Complex 112 12.2 Civil Society’s Responses to the Militarization of AI 115 Chapter 13: AI Agents and Agentic Systems 118 13.1 AI Agents in 2025: Between Promise and Accountability 119 13.2 When AI Begins to Act on Its Own 122 Chapter 14: Democratic AI – Community Control and Open Models 125 14.1 Learnings for Canada: Community-led AI in an Age of Democratic Decay 126 14.2 From Accessible Models to Democratic AI 129 14.3 Open Science Practices for Democratic AI 131 DOI: 10.5281/zenodo.17328882 8
INTRODUCTION November 2025 0.2 Philosophy, AI Ethics and Practical Implementations By Marianna B. Ganapini, PhD , Montreal AI Ethics Institute & University of North Carolina at Charlotte As AI systems become ubiquitous in daily life, we have been stuck in an antagonistic debate about AI and AI ethics, obscuring the importance of fostering human capabilities and flourishing for AI adoption and expansion. Because AI is so pervasive in all aspects of human life, if AI fails to deliver its promise of a better life, the AI project may well be unsustainable in the long-term. The goal of this SAIER Volume 7 is to offer a way to move beyond the antagonism of “innovation vs. ethics” towards a new way of thinking about these issues. On the one hand, AI has been framed as an opportunity leading to efficiency gains, productivity, competitive advantage and, for some, even human greatness. Subsequently, ethical issues surrounding AI are often perceived as hindrances rather than opportunities. On the other hand, many AI skeptics and AI pessimists describe this technology by mostly stressing the ethical and societal harm that comes from it. From moral bias to privacy violations, AI is a wrecking ball of ethical risks that promises to systematically undermine fundamental rights, increase discrimination, exploit cheap labor, destroy the environment and so on. These opposing views leave AI and AI ethics in tension. If ethics means constraints on risk while opportunity means maximizing adoption, companies will be inclined to either experience ethics as a problem or see AI adoption as too risky. To break this gridlock, a new way of thinking around AI can highlight how AI’s own long-term economic sustainability depends on its potential to increase wellbeing. AI needs to avoid what I call a “capability-erosion feedback loop,” where the use of sophisticated AI leads to loss of capabilities (financial, psychological, intellectual). As examples, consider three possible feedback loops where AI adoption may erode the very capabilities needed for AI to remain valuable: 1. The economic loop: If AI automates jobs without creating equivalent opportunities for capability development, workers lose both income and skills. 2. The cognitive loop: Research indicates that employees, students and many professionals often delegate thinking to AI systems. The result: declining ability to evaluate AI outputs and identify hallucinations as users become more dependent on tools that are at times wildly unreliable. 3. The psychological loop: AI systems optimize decisions across domains (e.g. what to watch, read, buy) and start carrying out tasks better than humans (e.g. write, paint, entertain). DOI: 10.5281/zenodo.17328882 15
INTRODUCTION November 2025 Once we take these trends seriously, we realize AI development and adoption require a different framework: AI as a key part of what we may call “the good life,” a life in which we can flourish as humans. This path treats ethics as capacity building. Instead of only asking “What AI must we avoid?”, we ask “Which human capabilities will this AI system expand, for whom?” We need to incorporate AI into our lives that actively makes us better off. Conclusion In conclusion, if AI is to endure, its legitimacy cannot rest on risk avoidance or profit generation alone. We need to reframe AI as an engine of growth and values for humans. Practically, this means procurement that introduces upskilling-by-design, AI ethics evaluation that tracks wellbeing alongside other metrics, and interfaces that cultivate judgment and skills rather than replace them. Companies that adopt this mandate will build systems people choose and want to use to improve their lives, and will see increased ROIs, as shown in this report. And at MAIEI we hope that this SAIER Volume 7 will be used as one of the tools to better understand how AI can concretely foster innovation and wellbeing at the same time. About the Author Marianna B. Ganapini, PhD is an Associate Professor at UNC Charlotte. She has collaborated with leading universities and companies, and is widely published in AI ethics and philosophy of AI. She is also Co-Founder of LogicaNow, a consultancy specializing in Responsible AI and AI governance. Marianna is the Faculty Director at the Montreal AI Ethics Institute, and a member of ISO and CEN-CENELEC JTC 21 working on AI standards. Cite this Article Ganapini, M. (2025) Philosophy, AI Ethics and Practical Implementations. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. Pp. 15-16. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 16
INTRODUCTION November 2025 0.3 Bridging Policy and Ethics: On the Launch of AI Policy Corner By Daniel S. Schiff, PhD, Governance and Responsible AI Lab (GRAIL) at Purdue University On March 18, 2025, for The AI Ethics Brief #160, we launched a new running series for the MAIEI newsletter called AI Policy Corner. AI Policy Corner is a space where we reflect on the translation of ethics into policy. Since then, we've published more than 15 short articles. We've covered topics like deepfakes, mental health, intellectual property, education, and frontier AI. We've focused on subnational policy developments with major implications, in US states like Colorado, Texas, and New York, as well as reflecting on industry developments and academic summits. And we've tried to bring special attention to how AI policy is unfolding around the world, in both countries with established AI leadership, like the US, Japan, South Korea and Singapore, while also surfacing important developments in regions less often followed in global AI ethics and policy conversations, like Turkey and Kenya. AI Policy Corner is a joint initiative of MAIEI and the center I co-direct at Purdue University, the Governance and Responsible AI Lab (GRAIL). GRAIL's research-focused and student-centered approach shapes the direction of AI Policy Corner in important ways. Most editions emanate directly from student research assistants who analyze policy documents from AGORA (AI Governance and Regulatory Archive), a collaboration between GRAIL and Georgetown University's Center for Security and Emerging Technology (CSET), hosted at the Emerging Technology Observatory. As the website describes, AGORA is “a living collection of AI-relevant laws, regulations, standards, and other governance documents from the US and around the world,” which “includes summaries, document text, thematic tags, and filters to help you quickly discover and analyze key developments in AI governance.” Before getting into the policy documents in more detail, it's perhaps prudent to speak about the motivation for AI Policy Corner. First, it reflects our belief that policy is the instantiation of ethics into the public will – how our beliefs about intellectual property, sustainability, global inequality, meaningful work, international security, and so on – move from research and advocacy into practice. Of course, policy is not the only channel through which ethics has an impact, but it is one of the most significant ones, hence GRAIL's focus on both AI ethics and AI policy. Only by reflecting on policy, I might argue, can we know whether our ethical visions are coming to pass. The second motivation for AI Policy Corner is more personal. Followers of The AI Ethics Brief will know that MAIEI has been working diligently to sustain the legacy of its co-founder, Abhishek Gupta, who passed away a year ago and is honored in this issue. DOI: 10.5281/zenodo.17328882 17
INTRODUCTION November 2025 This is a goal I strongly share: Abhishek was one of the voices I admired above all in the AI ethics space, with the Brief and State of AI Ethics Report constituting amongst the most important contributions in all of AI ethics discourse. He and I began working as industry leaders in AI ethics at the same time, Abhishek serving as Director for Responsible AI at Boston Consulting Group, and I as Responsible AI Lead at JPMorgan Chase. While complex positions for ethicists to inhabit, it reflected our shared belief that AI ethics needs to be instantiated in practice, and afforded us the opportunity to collaboratively troubleshoot on the complex, practical, and organizational dynamics in play. As a peer and kindred soul, his loss was greatly felt. Although I fear his voice and brilliance are irreplaceable, his contributions and spirit motivated us to safeguard this important work. AI Policy Corner thus represents our attempt to help sustain Abhishek’s legacy and impact, advancing MAIEI’s work. A little bit more about AI Policy Corner and why we find it to be a meaningful project, though we are most open to feedback! Perhaps most meaningful to us is that the authors of each issue are students. Typically, one undergraduate or graduate student serves as first author, with a second individual serving as reviewer. Students bring their own voices, identifying topics through discussion and based on issues they see as important or neglected, sometimes connected with their own particular interests in AI ethics or home countries. The student analyses don't just come from the void. Students analyze policy documents from the aforementioned AGORA database, not limited to laws or regulations, according to a structured codebook and annotation process. Two researchers cross-validate each entry, giving student authors deep familiarity with both specific policies and the broader governance landscape. In our weekly discussions, we reflect on both the content of these policy documents and the surrounding politics: which new ethical topics are rising to the forefront, which bills are or are not enacted and why, how industry strategies respond to public concerns, the dynamics of the AI ethics and safety communities, and so on. Thus, while the primary audience of AI Policy Corner is the readership of the Brief, we are especially keen to witness the learning of students and see their unique voices in the public sphere as they become champions of AI ethics. Our talented team of student authors has gone on to graduate school, interned at the offices of elected officials, and worked in industry, themselves becoming leaders of student academic communities focusing on AI ethics. Going forward, we hope that AI Policy Corner is useful to the community and provides viable insights. We welcome feedback on the content and approach of the Corner, and how we might provide more benefit. Thank you for reading. DOI: 10.5281/zenodo.17328882 18
INTRODUCTION November 2025 About the Author Dr. Daniel Schiff is an Assistant Professor of Technology Policy at Purdue University and the Co-Director of GRAIL, the Governance and Responsible AI Lab. As a policy scientist, he studies the formal and informal governance of AI through policy and industry, as well as AI's social and ethical implications in domains like education, labor, misinformation, and criminal justice. Daniel was the founding Responsible AI Lead at JP Morgan Chase and Secretary of IEEE 7010-2020, the first AI ethics standard. Cite this Article Schiff, D.S, (2025) Bridging Policy and Ethics: On the Launch of AI Policy Corner. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 17-19. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 19
State of AI Ethics Report | Volume 7 November 2025 PART I: FOUNDATIONS & GOVERNANCE Chapter 1: Global AI Governance at the Crossroads Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics Chapter 3: From Principles to Practice – Implementing AI Ethics in Organizations DOI: 10.5281/zenodo.17328882 20
PART I: FOUNDATIONS & GOVERNANCE SAIER Vol. 7 | Nov 2025 Chapter 1: Global AI Governance at the Crossroads 1.1 Competing AI Action Plans: Regional Bloc Responses to the US and China By Jimmy Y. Huang, MAIEI and McGill University 1.2 What "AI Sovereignty" Means for Nations Without Superpower Resources By Renjie Butalid, Montreal AI Ethics Institute (MAIEI) DOI: 10.5281/zenodo.17328882 21
Chapter 1: Global AI Governance at the Crossroads PART I | SAIER Vol. 7 | Nov 2025 1.1 Competing AI Action Plans: Regional Bloc Responses to the US and China By Jimmy Y. Huang , McGill University and MAIEI The past few years have been marked by explosive growth in worldwide AI capabilities and adoption, most notably in business, science, media, and personal use. On July 23, 2025, Washington, DC, unveiled the US AI Action Plan, characterized by a low-regulation strategy for AI innovation. Within days, on July 26, 2025, Beijing published the Global AI Governance Action Plan. Summarized in The AI Ethics Brief #170, China's plan calls for multilateral cooperation and governance on AI technology, contrasting sharply with the US plan's protectionist policies and domestic deregulation for AI firms. Earlier that month, the Trump administration rebranded the US AI Safety Institute (AISI), a body within NIST charged with managing AI risk, to the Center for AI Standards and Innovation (CAISI). Among CAISI’s new objectives: "to ensure U.S. dominance of international AI standards." The shift from “safety" to "standards" wasn't semantic. It signaled that AI governance had become an instrument of geopolitical competition. Strategic Hedging in Practice Middle powers and regional blocs, most with their own AI frameworks, have been strategically aligning themselves or hedging commitments to these competing visions. While fragmentation of AI policy is an inevitable outcome of countries seeking to differentiate and promote domestic innovation, the proliferation of disparate governing bodies and frameworks has arguably grown distracting rather than guiding. In October 2024, OECD’s Futures of Global AI Governance warned that fragmentation in AI policy could "hamper international interoperability, raise or exacerbate risks to human rights and democratic norms, pose barriers to trade and investment, and reduce the diffusion of benefits of trustworthy AI applications." The UN’s September 2024 Governing AI for Humanity report echoed this concern, noting that "coordination gaps between initiatives and institutions risk splitting the world into disconnected and incompatible AI governance regimes." The reality on the ground, however, is more complex than binary alignment suggests. Regional blocs aren't simply choosing sides; they're hedging, adapting, and negotiating autonomy where possible. African Union (AU) The AU officially supports an Africa-centric framework with the Continental Artificial Intelligence Strategy, published in July 2024. The AU's 55 member states present a diverse landscape of alignment. Kenya joined the US-launched International Network of AI Safety Institutes (INAISI), while Egypt hosted Chinese telecom firm Huawei's DOI: 10.5281/zenodo.17328882 22
Chapter 1: Global AI Governance at the Crossroads PART I | SAIER Vol. 7 | Nov 2025 Cloud Summit Northern Africa in July 2025. This divergence reflects both the AU's broader infrastructure ties, with several member states adopting Chinese-built telecom infrastructure via the Digital Silk Road (Dahdal & Abdel Ghafar, 2025), and the pragmatic reality that complete alignment with either superpower risks economic vulnerability. Since the July 2025 publication of competing action plans, no official AU-level response has been given. Association of Southeast Asian Nations (ASEAN) ASEAN's 10 member states are similarly navigating between frameworks. In February 2025, during the Paris AI Action Summit, Singapore, in close collaboration with Japan and fellow INAISI members, published a Joint Testing Report providing results on multilingual evaluations of LLMs. Later in July, ASEAN's Secretary-General, Dr. Kao Kim Hourn, delivered opening remarks at China's World AI Conference (WAIC) in Shanghai, the same day Beijing published its Global AI Governance Action Plan. In May 2025, the inaugural ASEAN-GCC-China summit yielded a joint statement that explored collaboration opportunities in the digital economy and AI partnerships. These aren't contradictions; they are survival strategies for maintaining policy sovereignty while securing economic partnerships. Council of Europe (COE) In September 2024, ten members and three non-members of the CoE signed the Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (CETS No. 225). Six more nations have signed in 2025. While the CoE is broadly aligned with the diplomacy track in the US's 2025 Action Plan, the July 2025 rebranding of US AISI to CAISI, with its shift from safety evaluation to ensuring US dominance of international standards, represents a fundamental divergence from the CoE's emphasis on evaluating AI risk to human rights. China is not currently party to the CoE AI Convention, leaving the framework's global reach limited. Gulf Cooperation Council (GCC) The GCC offers perhaps the clearest case study in strategic hedging. In June 2025, the UAE publicly declared its intention to fast-track a strategic AI partnership with the US, and Microsoft invested $1.5 billion USD in UAE-headquartered G42, replacing its previous involvement with Chinese firm Huawei. Yet Saudi Arabia hedges its position by signaling alignment with the US while simultaneously investing in Chinese AI capabilities. In May 2024, Prosperity7 Ventures, a Saudi fund, participated in a $400 million USD funding round for Chinese AI firm Zhipu AI. This bifurcated approach reflects the Gulf states' recognition that exclusive alignment with either power limits strategic flexibility. DOI: 10.5281/zenodo.17328882 23
Chapter 1: Global AI Governance at the Crossroads PART I | SAIER Vol. 7 | Nov 2025 Fragmentation as Strategy While not exhaustive, these snapshots illustrate the current global state of AI governance. Some countries have explicitly aligned, but regional blocs are broadly hedging their positions in the US-China race for AI capabilities and standard-setting. The question is whether strategic hedging can translate into genuine autonomy or merely delays inevitable pressure to choose. What's emerging isn't binary alignment but a complex web of partial commitments, parallel memberships, and calculated ambiguity. Middle powers recognize that AI governance frameworks encode power: determining whose values shape development, whose industries benefit from regulatory alignment, and whose communities bear the costs of misalignment. When the world's largest AI powers frame governance as a zero-sum competition, smaller nations face impossible choices: align with one power and risk alienating another, or attempt to pursue independent paths with limited resources and influence. Whether 2025's geopolitical competition catalyzes coordination or entrenches fragmentation remains an open question, one that middle powers and regional blocs are actively shaping through their strategic responses. About the Author Jimmy Y. Huang is an AI Ethics researcher and a recognized leader in the financial technology sector. An advisor to the Montreal AI Ethics Institute (MAIEI) and a previous B20 delegate within the G20 ecosystem, Jimmy has contributed to global policy discussion on responsible innovation and digital transformation. Jimmy bridges the gap between academia and international policy with practical implementation, advocating for transparency and accountability in an AI-driven landscape. Cite this Article Huang, J.H. (2025) Competing AI Action Plans: Regional Bloc Responses to the US and China. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 22-24. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state DOI: 10.5281/zenodo.17328882 24
Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics PART I | SAIER Vol. 7 | Nov 2025 About the Author Renée Sieber is an associate professor at McGill University, jointly appointed in Geography and the Bieler School of Environment. She studies the intersection of participatory theory and computational technologies. Renée is best known for her work on public participation in Geographic Information Systems and increasingly civic participation in AI. She was named 2025’s 100 brilliant women in AI Ethics. Cite this Article Sieber, R. (2025) The Institutions behind the concepts. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 29-31. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 31
Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics PART I | SAIER Vol. 7 | Nov 2025 2.2 The Contested Meanings of Responsible AI By Fabio Tollon , University of Edinburgh What should we make of the “responsible” in “responsible AI” (R-AI)? It seems like the best fit for this kind of responsibility is to understand it as a term of praise. When we talk about ‘responsible’ AI, we are envisioning the AI technology in question as having been developed or deployed in a way that was commendable and trustworthy. This could mean it meets certain fairness, safety and transparency criteria or is in the service of some public good. Of course, this is not the same meaning as when we say “she is a responsible parent,” but something similar is being tracked. Namely, that something was done in a way that we would think of as “good,” or at least aspiring towards that which is good. The insights below draw on the BRAID UK report on the R-AI ecosystem. However, R-AI is not just a simple term of praise, but a contested idea that stands for a number of different and at times contradictory things. It sometimes refers to a growing interdisciplinary field of critical AI research: at times, a governance ambition, and at others, a process for producing desirable AI products. Yet R-AI research often criticizes the R-AI governance agenda; meanwhile, many proposed ways to build responsible AI products lack buy-in from AI industry leaders. Thus, the ambiguity comes from the ways the term has been differentially attached to various practices in the AI space, with stakeholders deploying it in different ways. By understanding how these different meanings hang together, we can get a better handle on what we want R-AI to mean. R-AI as an Interdisciplinary Research Agenda Research under the banner of R-AI has been carried out by industry since at least 2017, when Microsoft and others first began using the term to brand their new algorithmic fairness, privacy and transparency toolkits. Industry R-AI researchers quickly engaged with academics in the closely related field of AI ethics, as well as public sector efforts to develop more “trustworthy” AI, and nonprofit/civil society researchers studying AI-driven harms. To be engaged in “responsible” AI from this perspective, then, is to be engaged in research that cuts across disciplinary and sectoral boundaries in order to address ethical and societal concerns emerging from AI. R-AI as a Stated Governance Ambition R-AI as a governance ambition originated from a desire (and sometimes a need) for tech companies to self-regulate. Since 2017, Meta, Google, Microsoft, IBM, PwC and Accenture have all produced internal R-AI documents, which each offer a set of principles and/or core values. These Responsible AI principles are used to inform the development of various tools and practices, such as internal ethics reviews, risk assessments, and product testing, in the hopes that these will realise particular values within their AI business. Soon after, nations began to frame Responsible AI as a government ambition, DOI: 10.5281/zenodo.17328882 32
Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics PART I | SAIER Vol. 7 | Nov 2025 part of their own innovation strategies. R-AI under this banner refers to a body of effective internal governance procedures, guidelines and guardrails for aligning AI innovation with the values and principles that corporations or governments want to signal to others that they stand for. R-AI as a Desired Type of AI Product Outside of particular corporate, government, and research agendas, there is an interest in developing a single reliable system or comprehensive set of standards and techniques for ensuring that AI products and services are ‘socially benign’ or have responsible characteristics. Here, the target is the technology rather than the developer, user or organisation. R-AI as an Ecosystem of Contested Meanings So, which is the “correct” meaning of R-AI? All of the above, one of them, or none? One way to approach this issue is to reframe it: What if we think about R-AI as a broad community or ecosystem of stakeholders? Instead of conceiving of R-AI in silos, we can tease out the ways these conceptualizations shape one another and we can aim towards a better and more holistic perspective on R-AI. Understanding R-AI in this way allows us to see that the three different meanings outlined earlier hang together, and that there are better or worse ways for them to feed into one another. That is, they each pick out important parts of what a truly ‘responsible’ AI ecosystem might look like, but none by themselves are enough. No singular part of an ecosystem is completely isolated from the rest, and so these composite ‘ecologies’ need to be mapped, managed, and supported in ways that enable flourishing across the whole. The ecological metaphor lets us formulate the holistic goal of Responsible AI, one that aims at a future state of affairs in which responsibility appropriately infuses and guides the complex interactions between the diverse and vast community of actors with a stake in AI and its societal and planetary impact. The key insight from this initial survey of the different meanings of R-AI is that it is not a singular concept or effort with a fixed meaning and clear definitional boundaries, but a complex and dynamic ecosystem pervaded by tensions and interdependencies. DOI: 10.5281/zenodo.17328882 33
Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics PART I | SAIER Vol. 7 | Nov 2025 About the Author Fabio is a philosopher of technology with interests in the ethics of AI, moral responsibility, and free will. He is a postdoctoral researcher part of the BRAID (Bridging Responsible AI Divides) program at the University of Edinburgh. He is a research fellow at the unit for the ethics of technology at Stellenbosch University and a research associate at the Centre for Artificial Intelligence Research (CAIR) at the University of Pretoria. Cite this Article Tollon, F. (2025) The Contested Meanings of Responsible AI. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 32-34. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 34
Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics PART I | SAIER Vol. 7 | Nov 2025 2.3 The Evolving AI Safety Conversation: Singapore’s Practical Path Forward By Wan Sie Lee, Infocomm Media Development Authority of Singapore A Shift in Global Cooperation on AI Safety Looking back at 2025, the AI Action Summit, held in Paris in February, represented an inflection point in the global discourse on AI safety, marking a shift in the dynamics of international cooperation. The two preceding global summits, Bletchley Park (2023) and Seoul (2024), established a consensus centered on mitigating catastrophic risks posed by frontier AI. However, the Paris Summit’s expansion of a narrow safety agenda to a broader one that encompasses economic opportunity, global equity, and industrial strategy, as well as the US’s announcement on prioritising diffusion of US frontier AI capabilities, signaled a de-emphasis on AI safety. While some momentum for global collaboration on AI safety is lost, balancing the narrative with innovation and adoption allows for more inclusive participation globally. For Singapore, collaboration and partnership continues to be important to address risks arising from the rapid advancement of AI. We focused on mechanisms to do this productively and practically. These are along three non-competitive and apolitical vectors: supporting technical cooperation within the expert community, facilitating the development of best practices and standards in testing and evaluation, and contributing to global capacity building. Technical Safety and Research Consensus The universal need for a shared, scientific foundation for AI safety allows for productive cooperation that transcends national political agendas. Building on the International AI Safety Report 2025, the Singapore Consensus on Global AI Safety Research Priorities convened more than 100 AI experts from around the world to exchange ideas and clarify urgent needs for technical AI safety research. The resulting research priorities are organised into three interlinked domains: risk assessment (evaluating risks before deployment); development of trustworthy, secure, reliable systems (during design and build phases); and control, monitoring and intervention (post-deployment). These form a shared agenda and provide a technical roadmap for collaboration within the scientific community that is essential, regardless of national political and regulatory philosophy. Best Practices and Standards in Evaluation Advancing measurement science and practices will provide the empirical foundation for evaluating risks. Current benchmarks and testing methods are fragmented. Developing shared and continuously updated evaluation frameworks will allow AI to be tested under consistent and DOI: 10.5281/zenodo.17328882 35
Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics PART I | SAIER Vol. 7 | Nov 2025 robust conditions, supporting cross-border comparability and transparency. Establishing reliable methodologies and standardised metrics, analogous to those in aviation or pharmaceuticals, would make safety claims testable, enabling cooperation between governments, industry and researchers, and ultimately supporting AI adoption. As part of the International Network of AI Safety Institutes, the Singapore AISI continued to lead joint testing efforts within the Network, working with the other AISIs to develop common evaluation methodologies for frontier models. The latest of these joint testing exercises focused on systemic safety behaviours of AI agents in areas like cybersecurity, data leakage, and fraud. It also included multi-lingual evaluation, drawing on the diverse capabilities within the Network. To support the greater use of AI, evaluations of AI safety also need to address the reliability and trustworthiness of deployed AI applications and systems, tackling societal risks and tangible, near-term harms. To do this, the Global Assurance Pilot brought together AI deployers and testers to develop testing standards for AI systems. They looked at ways to address risks in AI deployment in healthcare, financial services and other contexts, setting the foundation for global standards in AI application testing. Safety as Inclusion and Access Post-Paris, inclusive access and capacity-building are now critical components of the safety agenda. AI safety cannot be achieved by a few advanced economies alone. Through shared testing facilities, open research tools, training programmes, and technical partnerships, capability-building enables all nations to adopt safe and reliable AI. At the UN level, digital cooperation in AI governance is gaining increasing prominence. Singapore is doing its part to support this work, driving work within our region to develop AI safety frameworks and evaluation capacity that reflect diverse social contexts and languages, and reflect local realities. This also improves the quality and resilience of safety outcomes, as diverse perspectives help identify harms that might otherwise go undetected. As part of the Forum of Small States, we cooperated with countries that are members of the Forum to put out materials and resource guides that could be useful for other countries; for example, working with Rwanda to create an AI Playbook for Small States. We also set up Singapore Digital Gateway as a platform to share our resources and experience in AI safety, such as culturally-relevant models like Sea-Lion and open-source testing tools like AI Verify. DOI: 10.5281/zenodo.17328882 36
Chapter 2: Disentangling AI Safety, AI Alignment and AI Ethics PART I | SAIER Vol. 7 | Nov 2025 Looking Ahead to 2026 In 2025, the global tone for AI safety has shifted. The upcoming India AI Impact Summit in early 2026 is likely to solidify this trend, shifting the conversation even further from "action" to measurable "impact," with themes centred on inclusive development, sustainability, and democratising resources. While the UN platforms, including the upcoming scientific expert panel, will continue to align AI innovation with tangible global development goals, the world is less likely to see a rapid global treaty on AI safety. With geopolitical fragmentation remaining a reality, the path forward for governing this transformative technology lies in scaling these vectors of collaboration: the shared, non-political commitment to technical standards and assurance, combined with a focus on capacity-building and AI for public good. About the Author Lee Wan Sie is Cluster Director for AI Governance and Safety at Singapore’s Infocomm Media Development Authority, and Executive Director of the AI Verify Foundation. She drives Singapore’s approach to AI governance, helping to grow a reliable AI ecosystem and collaborating with governments around the world to further trustworthy AI development and adoption. She also leads policy in Singapore’s AI Safety Institute, where she defines Singapore’s AI safety policies, sets AI safety R&D priorities, and establishes global partnerships Cite this Article Lee, W.S. The Evolving AI Safety Conversation: Singapore’s Practical Path Forward. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 35-37. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 37
PART I: FOUNDATIONS & GOVERNANCE SAIER Vol. 7 | Nov 2025 Chapter 3: From Principles to Practice – Implementing AI Ethics in Organizations 3.1 AI Governance in Practice: 2025 Trends in Understanding and Implementation By Ismael Kherroubi Garcia, Kairoi, RAIN and MAIEI 3.2 Monetization and Closing the Principles-to-Practice Gap By Joahna Kuiper, HiirAI 3.3 From Solidarity to Practice: Building Ethical AI Capacity in Africa By Shi Kang’ethe, AIVERSE DOI: 10.5281/zenodo.17328882 38
Chapter 3: From Principles to Practice PART I | SAIER Vol. 7 | Nov 2025 3.1 AI Governance in Practice: 2025 Trends in Understanding and Implementation By Ismael Kherroubi Garcia , Kairoi, RAIN and MAIEI When we hear “governance,” we often think of regional, nationwide or international policies, after all, that is where governmental bodies operate. This section is not about that governance but the much more relatable policy structures we find in the workplace; across businesses, schools, hospitals and charities; organisations large and small. At this level, national and multinational initiatives may seem quite abstract; after all, why would the EU’s AI Act affect me if I simply use AI chatbots to write emails? And how could the UN’s independent international scientific panel on AI be relevant to, say, a bakery or a marketing agency? And yet, those multinational initiatives respond precisely to years of signals from the wider business ecosystem; years of entrepreneurs and organisational leaders calling for clarity as to how to best approach AI in an everchanging world. These are the signals that the present article attempts to tap into, seeking to understand not how policy-makers are responding to calls for clarity, but to understand how organisations are creating clarity for themselves in a world where policy seems to be lagging behind. AI Governance is a Business Necessity Regardless of the sector in which an organisation operates, it cannot avoid the AI conversation and its implications. AI chatbots have now been readily available to the public for three years. This means that employees may use such chatbots for work-related tasks. These tasks, in turn, are fundamental to work across sectors: AI chatbots may be used for writing, brainstorming, correspondence, summarising texts, and so on. So, how many people are using AI chatbots at work, and is it helpful? The evidence is unclear. In February 2025, Pew Research Center reported that, in the US, “relatively small shares of workers say they have used AI chatbots for work: 9% say they use them every day or a few times a week, and 7% say they use them a few times a month. [...] Among workers who have used AI chatbots for work, 40% say these tools have been extremely or very helpful in allowing them to do things more quickly. A smaller share (29%) say they have been highly helpful in improving the quality of their work.” (Lin & Parker, 2025). Meanwhile, a report from the Danish Bureau of Economic Research concluded in May that “AI chatbots have had no significant impact on earnings or recorded hours in any occupation” (Humlum & Vestergaard, 2025). Against this stands a global study conducted by the University of Melbourne and KPMG, which suggests that over 50% of workers use AI chatbots, and that their use leads to efficiency gains in over 60% of cases. Notwithstanding, the Australia-led study also emphasizes the risks that come with this rapid adoption of AI in the workplace, evidencing that “almost half of employees admit to having used AI in ways that contravene organizational policies. This includes uploading sensitive company information into public AI tools” (Gillespie et al., 2025). DOI: 10.5281/zenodo.17328882 39
Chapter 3: From Principles to Practice PART I | SAIER Vol. 7 | Nov 2025 In this context, AI governance is a business necessity, as the risk of misusing AI tools or falling for the hype may become costly. As Ganapini & Butalid (2025) explain in Tech Policy Press, “AI systems introduce operational, reputational, and regulatory risks.” With this, risk management mechanisms become central to protecting business interests; they respond to “market incentives” and remain consistent with pressures from regulators and consumers or beneficiaries. AI Governance is More than Compliance The pressures rendering AI governance a business necessity, market incentives, regulations and public influence, help explain that it is a question that goes beyond compliance alone. During a panel discussion hosted by the Responsible Artificial Intelligence Network (RAIN) in London in October, the speakers pointed to the risk of AI governance backsliding into compliance. Leaning on the BRAID UK responsible AI ecosystem report from June (Tollon & Vallor, 2025), the speakers made the case that legislation may inhibit the otherwise holistic and reflective nature of responsible AI initiatives. In other words, rather than AI governance building on decades of responsible research and innovation literature and advocacy, its scope may be narrowed to a series of checklists that ensure legal compliance. Returning for a moment to the higher-level governance activities mentioned at the start, both the US and the UK have shown in 2025 a retreat from “responsible AI” to compliance in 2025, best demonstrated by their refusal to sign the Paris summit declaration on inclusive AI. In this regard and for the foreseeable future, it will fall to organisations to design and implement AI governance strategies; to approach AI responsibly and with an eye to the societal impacts of their AI-related decisions; to seek independent advice and to promote AI literacy. About the Author Ismael is the Founder and CEO of Kairoi, the AI governance consultancy. He is also the Founder of the Responsible Artificial Intelligence Network (RAIN), and Participant Panel and Ethics Advisory Committee member at Genomics England. Ismael holds a master’s in Philosophy of the Social Sciences, where he sought to uncover enabling conditions for multidisciplinary collaboration in scientific projects. As a result, a key tenet of his work is to advocate for an epistemic humility. Cite this Article Kherroubi García, I. (2025). AI Governance in Practice: 2025 trends in understanding and implementation. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 39-40. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 40
PART II: SOCIAL JUSTICE & EQUITY SAIER Vol. 7 | Nov 2025 Chapter 4: Democracy and AI Disinformation 4.1 Legislating the Moving Digital Terrain By Rachel Adams, Global Center on AI Governance and Leverhulme Centre for the Future of Intelligence, University of Cambridge 4.2 AI and the Body Politic By Linda Solomon Wood, Canada's National Observer 4.3 Reinforcing the Feedback Loop: How AI in Elections Deepens Democratic Inequities By Seher Shafiq, Mozilla Foundation DOI: 10.5281/zenodo.17328882 47
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 4.1 Legislating the Moving Digital Terrain By Rachel Adams , Global Center on AI Governance and Leverhulme Centre for the Future of Intelligence, University of Cambridge Our information systems, the cornerstone of informed public discourse and effective governance worldwide, have faced unprecedented challenges over the past two years. This period has seen a confluence of factors, including the proliferation of generative-AI fueled misinformation and disinformation, the rise of echo chambers fueled by social media algorithms, and a growing distrust in traditional media institutions. 2024 alone became the “super-election” year, with more than four billion people eligible to vote their political representatives in India, the EU, the UK, South Africa, Mexico, Indonesia, and the US. The recent strains on public information systems have collectively tested the resilience and integrity of how citizens in countries around the world access, process, and interpret information vital for democratic participation. We saw what many expected: synthetic robocalls, cloned voices, and fabricated videos; but also what fewer anticipated: the more ambient harms of low-grade synthetic “slop” saturating feeds, and a spreading uncertainty about what can be trusted at all. In India’s mammoth election, political operatives industrialized voice cloning and personalized AI videos across dozens of languages; Bollywood deepfakes went viral; and even avatars of deceased leaders “returned” to endorse successors. Meanwhile, in South Africa, deepfaked endorsements and threats characterised the pre-election period, including from then US President Joe Biden supposedly promising sanctions if the ruling African National Congress won power again. And in the midst of these information crises, Meta decides to remove its fact-checkers and reduce its content moderation functions across Facebook and Instagram. Across the Majority World, fact-checking networks like AfricaCheck became first responders, often with minimal resources. Their work is demonstrative of the critical gap that major social networking companies leave in monitoring inflammatory and malicious content outside of North America and Europe. Unlike in wealthier democracies, in addition to a lack of meaningful content moderation from the major platforms, the local resources for detection, moderation, and civic education are often thin. For communities in the Global Majority, the stakes of AI-powered disinformation are arguably higher. Many societies already contend with fragile trust in institutions, limited press freedoms, and stark inequalities in access to accurate information. In such environments, even modest volumes of AI-generated disinformation can tip the scales, inflame ethnic tensions, or suppress turnout. DOI: 10.5281/zenodo.17328882 48
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 Policy Response Platforms and providers scrambled to show responsibility. 27 major tech companies, including OpenAI, Google, Meta and TikTok, signed A Tech Accord to Combat Deceptive Use of AI in 2024 Elections in Munich, pledging to curb deceptive AI in elections, invest in provenance tools, and coordinate responses. Critics called it voluntary and uneven. Significantly more concrete, on August 1st, 2024, the EU AI Act entered into force with phased obligations which explicitly covered deepfakes, including transparency duties for systems that generate or manipulate image and audio. Together with the transparency obligations for large platforms and responsibilities to undertake risk-mitigation measures under the EU’s Digital Services Act, Europe has begun to hard-wire information-integrity duties into law rather than rely on the voluntary measures of platforms. Whether those duties can be enforced consistently and globally, particularly across regions in the Global Majority where platform oversight is badly needed, is the question that now matters. Looking Ahead Looking ahead, four trends will determine whether democracies adapt or falter in the age of AI. 1. The evolution of regulatory baselines. Will countries outside Europe adopt binding standards for disclosure, labelling, and liability? Or will the Global Majority remain subject to the uneven spill-over of Western rules? The spread of enforceable norms tailored to local contexts, will be decisive. 2. The future of provenance and authenticity. Watermarking, content credentials, and authenticity infrastructure are advancing rapidly. Yet unless these tools become universal, interoperable, and verifiable by independent actors, they risk being another partial solution that creates a false sense of security. 3. Platform accountability in practice. The voluntary accords of 2024 were only a first step. The next phase will be whether platforms disclose enforcement data, open themselves to audits, and show consistent treatment across regions and languages. 4. Democratic resilience from below. The most overlooked determinant will be civic capacity. Investments in independent media, fact-checking networks, and public education are as important as any technical safeguard. Communities that can rapidly contextualise and debunk will blunt the force of synthetic disinformation. DOI: 10.5281/zenodo.17328882 49
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 Conclusion For Cory Doctorow, the problem is is not that the internet and social media is the most pressing singular concern of our time; rather, it is that these digital terrains are the site upon which all the other complex issues of today – inequality, genocide, racism – take place and are mediated. The events of our recent history have shown both the vulnerabilities and the resilience of democratic institutions and community efforts. As we move forward, the challenge is clear: to regulate not only the tools of manipulation but the practices that undermine collective agency, and to support communities where the risks are greatest. About the Author Rachel Adams, PhD, is the Founding CEO of the Global Centre on AI Governance. She is the author of The New Empire of AI: The Future of Global Inequality (Polity Press, 2024). She is an Assistant Research Professor of the Leverhulme Center for the Future of Intelligence, University of Cambridge, and an Honorary Research Fellow of The Ethics Lab at the University of Cape Town. She holds degrees in English Literature, International Human Rights Law and Philosophy. Cite this Article Adams, R. (2025) Legislating the Moving Digital Terrain. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 48-50. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 50
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 4.2 AI and the Body Politic By Linda Solomon Wood, Canada's National Observer "This website I'm quoting from, I don't know a whole lot about," Alberta councillor Patrick Wilson told Cochrane's council in June 2025. "But I just thought their words were better than mine." His motion to abandon the town's 20-year climate commitment nearly passed. The words weren't his, they came from an AI chatbot designed to kill climate policies. This moment reveals the systematic erosion of authenticity: AI systems that can generate convincing political messaging while concealing their artificial origins. In January 2025, Rory White brought a custom-built tool he’d built to Canada's National Observer (CNO), the investigative, climate-focused publication I founded in 2015. The tool, named “Civic Searchlight,” combed through YouTube archives of municipal meetings, reviewing transcripts of the meetings for unusual patterns. It didn't take long: councillors in different provinces,thousands of kilometres apart, were speaking in the same voice. Whole sentences lifted verbatim. Civic Searchlight revealed that a group called “KICLEI” was using an AI chatbot to flood Canadian municipal councils with climate misinformation. KICLEI deliberately named itself to mimic ICLEI, the legitimate international sustainability network. The AI-generated letters appeared to come from an environmental organization when they actually promoted anti-climate messaging. This represents what some of us had already suspected: using AI to scale deception at a level that was under the radar until Rory White detected it using Civic Searchlight and his investigative journalism skills. One AI system can saturate thousands of officials' communication channels simultaneously, each receiving personalized, locally relevant messaging that appears to come from concerned citizens. Traditional verification methods break down when AI can impersonate legitimate organizations while generating content that passes authenticity tests. The effectiveness of these campaigns led to measurable consequences. We documented councillors in Cochrane, Thorold, Pembroke, Peterborough, and Pickering quoting KICLEI verbatim in council meetings and preparing to vote against funding for projects aimed at lowering their municipalities' carbon footprints. Partners for Climate Protection ties member municipalities to the $1.65 billion CAD Green Municipal Fund, which finances retrofits, transit upgrades, and green infrastructure. When Thorold, Ontario, voted to withdraw, projects were shelved, grants put at risk, and jobs lost. If ten towns follow, that could mean tens of millions in cancelled contracts and hundreds of construction jobs wiped out. DOI: 10.5281/zenodo.17328882 51
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 Civic Searchlight enables researchers to track policy language across municipalities for any issue – housing, education, healthcare, immigration – anywhere identical talking points appear across jurisdictions. Seeing this motivated us to share the tool for free, so hundreds of people will now be able to find things. In the first week, 489 people signed up representing major Canadian media, universities, civil society groups, and municipal officials. We made the tool available to journalists, researchers, and civil society groups. We're still in beta mode, developing it based on what we learn from early adopters and there is a simple vetting process. By the first month, 560 people had signed up. “Our region has been a hot spot for misinformation campaigns against local climate action, and this excellent tool should help us stay informed for what is coming up at municipal meetings,” wrote one researcher to CNO after using Civic Searchlight.. A journalist wrote, “For fact-checking and research purposes. (This is an amazing tool!!)” Over the past eight years, the epidemic spread of technological capabilities of what we used to call "fake news" has been as dangerous and fast-moving as a global pandemic. In 2017, I described fake news as "a viral infection that threatens the body politic," arguing that "just as the human body's immune system relies on multiple layers of defence, we have to as well." But AI manipulation works differently, where fake news spreads through social media, AI doesn't need human amplification. It doesn't distract from local engagement; it infiltrates those information pathways directly. AI systems insert themselves directly into the channels councils depend on for citizen input. The threats posed by untethered AI disinformation campaigns extend beyond civil discourse to the climate, the very air we all breathe. When bad actors use AI to diminish the integrity of public discourse, organizations that can contribute to defense should do so. The tool becomes more valuable when widely used because defending democracy requires network effects. The councillor's preference for AI-generated talking points over his own judgment represents a broader challenge: if elected officials can't distinguish AI manipulation from citizen input, what happens to public deliberation? But how far we've come since 2017, and how fast. When I wrote about fake news as a viral infection, there was no ChatGPT, Claude, or Gemini to carry propaganda at lightning speed. The ethical questions get bigger every day. One thing Canada could do at the federal regulatory level would be to impose criminal penalties for using AI to impersonate legitimate organizations or people in political communications. As I understand it, this would require updating fraud laws for the AI era. Meanwhile, AI will readily execute any deception. All it takes is a prompt, a bot, and a creative person who wants to influence policy outcomes. Like the Trojan Horse, it destroys from within by masquerading as the authentic citizen voice democracy depends on. DOI: 10.5281/zenodo.17328882 52
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 About the Author Linda Solomon Wood is the founder and publisher of Canada's National Observer and launched the Democracy & Integrity Project in 2018, a CNO initiative to investigate and reveal disinformation. She is a frequent speaker and has led award-winning reporting on democracy and climate change for over two decades. Cite this Article Solomon Wood, L. AI and the Body Politic. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 51-53. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 53
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 4.3 Reinforcing the Feedback Loop: How AI in Elections Deepens Democratic Inequities By Seher Shafiq, Mozilla Foundation AI systems amplify the gamification of voter engagement, making marginalization a self-fulfilling prophecy. The gamification of Get Out the Vote (GOTV) efforts has been a longstanding source of exclusion for those who are already the most underrepresented in civic spaces, even before AI tools like augmented analytics and natural language processing became available to campaigns. In Canada, voter lists are compiled from Elections Canada data and refined through canvassing; categorizing voters based on their likelihood of supporting a particular party. Campaigns then prioritize "likely supporters" for volunteer outreach, creating a feedback loop that continually refines the list of potential supporters. The issue is that already marginalized communities are less likely to vote, and rarely make it onto campaigns’ “likely supporter” list, meaning that low-income and immigrant households, for example, do not have candidates knocking on their doors. For these demographics, this lack of candidate engagement reinforces the perception that politicians do not care about them, resulting in lower civic engagement and voter turnout among those groups. In Canada, this is especially true for racialized minorities. As efficient as voter lists are for campaigns, they "gamify" elections at the risk of impeding democratic engagement among those who are arguably most impacted by policy. GenAI catalyzes disinformation into full-scale disengagement of marginalized voters. If the structural issues with voter lists driving engagement weren’t enough, the issues of misinformation and disinformation, particularly in the last several years, have created chaos in the information ecosystem. Just weeks ago, the Andrew Cuomo campaign launched a racist AI-generated attack ad against Zohran Mamdani, depicting an AI version of him running through New York, eating rice with his hands. In Canada, political misinformation online has become uncontrollable. A 2025 report from Canada’s Media Ecosystem Observatory found that, in the lead-up to the federal election, more than one quarter of Canadians were exposed to sophisticated fake political content. After the election, more than three-quarters of Canadians reported they believe misinformation impacted the election. The Canadian Digital Media Research Network found that “Canada’s 2025 federal election upheld its integrity but exposed a digital ecosystem under mounting strain,” citing many examples of deepfakes, AI-generated DOI: 10.5281/zenodo.17328882 54
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 news, and bot activity. Even AI Chatbots used to verify the validity of information have resulted in incorrectly confirming fake content is real, aiding the spread of misinformation. AI in elections impacts public trust; community-based solutions can help. Public sentiment analysis on elections does not reflect the public. Even before AI tools were available to support and report on public sentiment analysis for elections, political polling in the lead-up to elections was an unreliable marker of real public sentiment, as only those already engaged went through the effort of completing phone polls, leading to results that represented a sliver of engaged Canadians. Reporting out this misleading information spurs a cycle of disengagement. In the 2022 Ontario provincial election, for example, many believed that there was no point in voting in the race because polling consistently indicated that incumbent Doug Ford would win. That election saw the lowest voter turnout in Ontario provincial history, sitting at 43%. Since then, AI has been added to the mix, often scraping online spaces to read public sentiment and reporting outwards as part of election coverage by media (see the AI tool, Polly, as an example). However, again, those who are most impacted by politics (immigrants, those with low literacy or lower English language skills) are often not engaging in those online spaces, and their sentiments are not captured as part of this analysis. What is reported as public sentiment in an election race is thus an inaccurate representation of real public sentiment, invisibilizing those who are already marginalized, and causing them to disengage further. Scaffolding against disengagement through community-based organizations. Tailoring voter engagement materials to specific demographics has been a best practice for community organizations (see Journeys to Active Citizenship at North York Community House, The Canadian-Muslim Vote, Apathy is Boring, Operation Black Vote Canada, and more). However, civic and democratic engagement efforts for marginalized groups in Canada have traditionally been severely underfunded. Some solutions to the unintended harms caused by AI systems in elections include: ● Better funding for grassroots community organizations to deliver digital literacy training. Doing so would arm those most disenfranchised in civic spaces to better identify mis/disinformation and find ways to meaningfully engage in the system. ● Enhanced funding and support for grassroots efforts to promote civic and voter engagement in marginalized communities. This could include bolstering online public sentiment analysis with in-person surveys or community focus groups so that reported public sentiment is more reflective of diverse perspectives. DOI: 10.5281/zenodo.17328882 55
Chapter 4: Democracy and AI Disinformation PART II | SAIER Vol. 7 | Nov 2025 ● While CSIS has noted that GenAI will undermine citizens’ trust in democracy, more needs to be done to update campaign rules in Canada to hold political campaigns accountable so that using AI systems to create impersonations or deepfakes is no longer allowed. A start would be to update the Elections Canada Act to cover GenAI and deepfakes, which has not yet been done. Without bolstering community-based initiatives, promoting digital literacy, and implementing concrete policy changes to the Elections Canada Act, AI will continue to exacerbate inequities in our democracy. As the Canadian Digital Media Research Network warns, Canada must “act now to protect future elections” from the impacts of AI. About the Author Seher leads global community engagement for Mozilla Foundation (the non-profit behind Firefox), where she focuses on building a better tech future that puts people first. Cite this Article Shafiq, S. (2025). Reinforcing the Feedback Loop: How AI in Elections Deepens Democratic Inequities. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 54-56. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 56
Chapter 5: Algorithmic Justice in Practice PART II | SAIER Vol. 7 | Nov 2025 ● The "Common Sense" Fix That Failed: An intuitive fix, building separate models for each ethnic group, was counter-productive. It actually amplified bias and produced the worst fairness outcomes tested. ● The "Statistically Fair, Practically Harmful" Fix: Another strategy forced the algorithm to produce statistically "fair" outcomes, ensuring, for example, that the percentage of students flagged from each group looked equitable. This technical "success" came at a devastating cost: the model's actual accuracy for Black students fell below the original baseline. This is a classic "fix" that harms the community it's intended to help by prioritizing an abstract number over practical utility. ● The "Apparent Win-Win" Fix: A third approach, adjusting the "at-risk" sensitivity threshold differently for each group, finally appeared to be a "win-win." It was one of the only methods that increased accuracy for Black students (from 61.4% to 64.2%) while also improving fairness metrics. But this "fix" introduced a new, practical trade-off: it sharply increased the overall number of students incorrectly flagged (the false positive rate). This shifts the burden from a data science problem to a resource allocation problem. Is the institution prepared to support the massive increase in students flagged for intervention? This is where we must move beyond the algorithm. The real "aha" moment came from a retrospective case study of an actual intervention trial. In the trial, students identified as at-risk received a supportive call from a tutor. The results were transformative. The intervention led to a 16.5% uplift in pass rates for White students. For Black students, the uplift was at 32.1%. This finding reframes the entire discussion. A high false positive rate is only a problem if the intervention is punitive, stigmatizing, or resource-intensive. But if the intervention is a low-harm, supportive call, the cost of a false positive is minimal. The risk of a false negative, missing a student who genuinely needs help and drops out, is far, far greater. What’s actually happening here is that an "imperfect" model, even one that "over-targets" a disadvantaged group, can become a powerful engine for equity if it is connected to an effective, well-designed, and non-punitive human intervention. The pursuit of algorithmic fairness, then, is not a purely technical search for the "right" metric; it is a sociotechnical design challenge. This case study provides clear, actionable insights. ● For practitioners and data scientists, stop optimizing for abstract fairness metrics in a vacuum. You must work with support teams to understand the intervention. Is it high-harm (like a disciplinary meeting) or low-harm (like a supportive call)? The answer changes whether you should prioritize minimizing false positives or false negatives. ● For university leadership and policymakers, your work is to build the system, not just buy the model. The real-world case study succeeded because the intervention was human-centric and non-punitive. Your investment in counselors, tutors, and ethical support structures is ultimately more important than the algorithm itself. DOI: 10.5281/zenodo.17328882 63
Chapter 5: Algorithmic Justice in Practice PART II | SAIER Vol. 7 | Nov 2025 ● For community leaders and educators, the key is to demand a seat at the table. This analysis proves that "fairness" is a context-dependent, normative choice, not a purely technical one. We must evaluate these systems not by their statistical properties, but by their tangible, real-world impact on students. About the Author Adnan Akbar, PhD, is an AI leader with 12+ years of experience , providing consultancy to architect scalable AI/ML solutions on AWS, GCP, and Azure. He is a leader in responsible AI, holding an MPhil in AI Ethics from Cambridge. Adnan helps companies scale AI ethically by establishing robust governance frameworks and reducing algorithmic bias. He is recognized as a DataIQ Future Leader for 2025 and endorsed as an Upcoming Future Leader in Data Science by the Royal Academy of Engineering. Cite this Article Akbar, A. (2025) Algorithmic Justice vs. State Power. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 62-64. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 64
PART II: SOCIAL JUSTICE & EQUITY SAIER Vol. 7 | Nov 2025 Chapter 6: AI Surveillance, Privacy, and Human Rights 6.1 AI, Surveillance, and the Public Good By Maria Lungu, University of Virginia 6.2 Challenging Mandated AI in the Public Sector By Roxana Akhmetova, University of Oxford 6.3 AI, Biometrics, and Canada’s Developing Legal Framework in 2025 By Jake Wildman-Sisk, Independent, Lawyer DOI: 10.5281/zenodo.17328882 65
Chapter 6: AI Surveillance, Privacy, and Human Rights PART II | SAIER Vol. 7 | Nov 2025 6.1 AI, Surveillance, and the Public Good By Maria Lungu , University of Virginia Artificial intelligence has become the silent architecture behind modern governance. From determining where police patrols are deployed to assessing eligibility for social services, AI increasingly influences decisions that affect everyday lives. Surveillance has been quietly normalized through algorithms framed as neutral tools of progress. Across the world, political systems diverge in their approach to AI and surveillance. Yet, some share a common fear that innovation is advancing faster than our capacity to protect the rights of those affected. Surveillance Infrastructure AI-driven surveillance has moved far beyond the classic frontier of restricted perimeters (e.g., airport checkpoints or dedicated “safe city” zones). Today, it is woven into everyday public life's “connective tissue”: traffic lights, open-street cameras, social service decision-making systems, classroom monitoring, smart-city infrastructure, and more. These systems are typically introduced under the rhetoric of efficiency and safety. However, the trade-off is often one of opacity and loss of agency. Many individuals are unaware of how their data is being collected, processed, and decisions are made on their behalf, or how they might contest those decisions. Generative AI deepens these tensions. Tools that synthesize text and images or create models of behaviour, what some call “data doubles”, enable organizations to build proxy representations of people based on their historical data. Once you move into such predictive or modelled terrain, the line between simulation and surveillance blurs because you are not just watching what someone does, you are acting on what a model suggests someone might do. Algorithmic Policing and Civic Profiling As AI surveillance expands, questions about its legitimacy and oversight are becoming more prominent, particularly in the US, where predictive policing or surveillance tools from Palantir and Flock Safety are increasingly incorporated into municipal contracts. Often described as “evidence-based modernization,” these systems raise important considerations about consent and the balance between innovation and public trust. Modern surveillance increasingly combines observation with predictive modeling, a shift some scholars refer to as “civic profiling,” in which citizens are viewed as data subjects whose behavior is interpreted through algorithmic systems. Understanding these dynamics is essential for designing transparent oversight mechanisms. DOI: 10.5281/zenodo.17328882 66
Chapter 6: AI Surveillance, Privacy, and Human Rights PART II | SAIER Vol. 7 | Nov 2025 Global Governance Divide While the US and its allies continue to deliberate over whether or not to regulate private-sector AI, China has pursued a centralized governance model. China’s Global AI Governance Initiative reflects China’s belief that state coordination and international harmonization can stabilize a rapidly evolving technological landscape. At the 2025 World AI Conference, Premier Li Qiang proposed an Action Plan that includes a 13-point roadmap for global AI coordination, with practical steps to implement the 2023 Global AI Governance Initiative. For China, a structured, state-led framework ensures alignment between innovation and national priorities, while offering predictability to foreign partners. Critics often describe this model as illiberal, yet its appeal lies in the clarity it provides, setting uniform standards and compliance expectations that reduce regulatory uncertainty. The US has opted for a more pluralistic approach rooted in its federal structure and tradition of market-driven innovation. The AI Action Plan reflects a policy judgment that overregulation could stifle competitiveness and hinder experimentation in emerging sectors. In this view, flexibility and decentralized governance allow states and agencies to tailor oversight to local contexts, preserving space for innovation while advancing ethical norms. However, this same flexibility comes with trade-offs. Regulatory inconsistency and jurisdictional overlap can create uncertainty about what constitutes “high-risk AI.” As a result, companies sometimes gravitate toward states with lighter oversight, complicating nationwide accountability and creating uneven protection standards across the public sector. Both approaches reveal different philosophies of governance rather than clear right or wrong answers. Communities Pushing Back Yet beneath the institutional lag, something remarkable is happening. Communities are no longer passive subjects of surveillance; they are becoming active designers of oversight. For example, in Charlottesville, Virginia, residents pushed back on the use of “Flock” cameras. The police department maintains that the system helps solve crimes and recover property, but critics argue it is overly invasive and primarily aids post-crime investigations. Community organizers held meetings, petitioned local government, and engaged directly with the police chief to ensure residents' concerns were heard and addressed. In Boston and Minneapolis, residents have called for algorithmic impact statements that evaluate privacy risk, public value alignment, and whether an AI system fairly serves community priorities. I like to think of this as a form of “algorithmic localism,” where accountability must be built from the ground up, not handed down from abstract frameworks. We do not want the scale and speed of surveillance to outpace democratic scrutiny. The allure of efficiency continues to eclipse the fundamental question: what kind of society are we optimizing for? DOI: 10.5281/zenodo.17328882 67
Chapter 6: AI Surveillance, Privacy, and Human Rights PART II | SAIER Vol. 7 | Nov 2025 Communities are experimenting with practical, replicable strategies to ensure AI serves the public good. Through participatory audits or even vetoes, residents actively review AI systems and propose modifications, shaping technology in ways that reflect local values. Advocacy groups also run transparency campaigns, using workshops and public forums to make complex technical systems understandable and accessible to residents. These approaches center grassroots expertise and lived experience, prioritizing the knowledge and voices of those most affected over abstract technical assumptions. By doing so, communities reclaim agency. About the Author Dr. Maria Lungu is a Postdoctoral Research Associate in the Digital Technology for Democracy Lab at the University of Virginia. She earned a Juris Doctor from the University of Tennessee College of Law and a PhD from Florida Atlantic University. A policy group member at the Center for AI and Digital Policy (CAIDP), she has also been recognized as an AI Ethics and Society (AIES) research fellow, and is a member of the World Economic Forum’s AI Governance Alliance. Cite this Article Lungu, M. (2025) AI, Surveillance, and the Public Good. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 66-68. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 68
Chapter 6: AI Surveillance, Privacy, and Human Rights PART II | SAIER Vol. 7 | Nov 2025 6.2 Challenging Mandated AI in the Public Sector By Roxana Akhmetova , University of Oxford 2025 was a defining year for AI. Governments worldwide began to structurally integrate AI into our daily lives, from mandatory digital identity cards, surveillance pricing, to predictive suspicion surveillance. AI is now becoming a condition of participation in modern society. Although AI has been used for surveillance for nearly a decade, it is often first deployed and tested on vulnerable populations like asylum seekers and welfare recipients, and in specific contexts like airports, border control, and public events where opting out of the service meant being denied that service. As we investigate these changes deeper, we can see how these systems evolved from targeted interventions into mandatory infrastructure, from experimental deployments to operational requirements for the masses. The shift toward predictive and autonomous decision-making is converging across previously separate systems. Throughout 2025, governments across the Global North and the Global South, including the UK, Nigeria, and China, Vietnam, Costa Rica, Nigeria, Zimbabwe, Mexico and Australia, made digital surveillance mandatory for accessing employment, banking, telecommunications, online services, and the web (see here and here). Biometric authentication requirements and digital identity cards are not novel technologies. These systems were tested on asylum seekers in refugee camps and border processing centers. In exchange for refuge and protection, asylum seekers were asked to surrender their biometric information, often with no meaningful consent (see here and here). In 2025, we are also seeing an expansion in AI surveillance capabilities. AI tools are used not just to record and watch, but to autonomously decide who warrants further inspection, who generates suspicion with limited human predicate. In March 2025, the US State Department’s “Catch and Revoke” program used AI to scan social media accounts of international students. Individuals flagged by the system had previously expressed Palestinian solidarity. Whether this was the determining factor in visa decisions remains unclear, but the system's opacity makes it impossible to rule out political targeting as a mechanism (see here and here). Brazil and Kenya implemented algorithmic systems in their social protection systems that automatically flag benefit recipients for investigation and risk assessment. The EU’s proposed “Chat Control” legislation pushed this further, suggesting platforms scan private messages for “suspicious” content before messages are sent. The examples suggest AI may be used not just for security, but also for political control; against speech that challenges power becomes algorithmically suspicious. Practitioners are called to recognise that what is marketed as mutual benefit, “you give data, you get convenience”, is a one-sided extraction and may be a transfer of power from individuals to institutions. Institutions gain surveillance capabilities and control over who has access to basic services. Individuals “gain” conditional access to what used to be unconditional rights and bear risks of our biometric data being breached, being excluded when systems fail, and every one of our interactions creating permanent digital trails that DOI: 10.5281/zenodo.17328882 69
Chapter 6: AI Surveillance, Privacy, and Human Rights PART II | SAIER Vol. 7 | Nov 2025 allow future profiling and control. The language of “convenience” and “efficiency” masks that we are losing rights, not gaining benefits. While verification problems are real, surveillance-as-a-solution intensifies the underlying dynamic: systems built on distrust require constant proof of trustworthiness, which justifies expanding surveillance infrastructure. This creates a self-reinforcing cycle where the “solution” generates the conditions that justify its expansion. We end up paying, through data and rights, for the “solution” to a problem the system itself created. At the end, we are left with no choice but to opt in or be socially excluded and lose access to employment, banking, services... It is Hobson's choice: participate in surveillance or exit society. The risk management frameworks for these systems rarely account for this coercion dynamic because they assume consent when individuals face constructed necessity. As more databases become linked together, a single data point can impact other systems. Access to banking and financial services increasingly depends on national digital ID systems that use biometric verification, employment verification tied to immigration status checks, and private-sector surveillance tools connected to government data systems. These convergence points are not inevitable; they are design choices. Practitioners who are building these systems should consider the source of the data that is fed into the algorithm. Algorithms learn from historical data which may reflect existing inequalities: neighborhoods that are already heavily policed, communities already flagged as threats, populations already presumed fraudulent (see here and here). A warning to practitioners: automation is not neutral. When you automate a process, you are encoding discretionary decisions into systems that are harder to audit, challenge, or appeal. This shifts power away from transparent human judgment, even imperfect judgment, and toward institutions that control the algorithm. Those who design algorithmic systems should consider building “friction” into the system by creating technical barriers to data sharing, such as hard breaks between datasets, making integration challenging, requiring explicit legal authorisation and not just administrative convenience. Communities should keep fighting to keep systems legally separate; the connections between systems are the danger, so break the connections. Challenge mandatory biometric enrollment which creates two-tier citizenship, especially where it means the loss of rights. The decisions made in 2026 will determine whether we will retain any power to remain incompletely known. About the Author Roxana is a doctoral researcher at the University of Oxford. Roxana studies how public institutions adopt and operationalise AI systems, particularly under conditions of regulatory uncertainty, private-sector dependence, and rapid model integration. Her work is also focused on the real-world bottlenecks in deployment, oversight, and institutional accountability, and how they might intensify as frontier AI systems become more capable and widely deployed across critical public services. Cite this Article Akhmetova, R. (2025) Mandated AI in the Public Sector and Challenging Inevitability. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 69-70. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 70
Chapter 6: AI Surveillance, Privacy, and Human Rights PART II | SAIER Vol. 7 | Nov 2025 6.3 AI, Biometrics, and Canada’s Developing Legal Framework in 2025 By Jake Wildman-Sisk, Independent, Lawyer Author’s Disclaimer: The views expressed in this publication are the author’s own and do not express the views of their employer or any other third party, nor do they constitute legal advice. AI systems that process biometrics, the quantification of human characteristics into measurable terms, increasingly became embedded in Canadians’ lives in 2025. Airports use facial recognition for security purposes, secure buildings use fingerprints or other unique physical identifiers to grant users access, and intelligent personal assistant tools like Siri and Alexa loyally respond to unique voiceprints. These tools offer speed, convenience, and accuracy, but their rise has triggered a wave of scrutiny, especially from a privacy perspective, given the sensitivity of uniquely identifying biometrics. At the heart of this scrutiny is a growing recognition that biometrics can reveal information that is intimately linked to individuals, often unique, and unlikely to vary significantly over time. A facial scan, for instance, can reveal not only identity but sometimes also race, gender, and health indicators. With the rise of AI, the processing of biometrics has become even more perilous. AI can rapidly process sensitive information, amplify biases, and scale surveillance in ways that were previously unimaginable. This year shone a spotlight on these risks when Canadian lawmakers, courts, and privacy commissioners grappled with these issues, yet questions persist about the extent to which existing, non-AI-specific laws can effectively address these challenges. The Rise and Fall of AIDA In 2022, the Canadian government took steps to regulate biometrics in AI systems by introducing the Artificial Intelligence & Data Act (“AIDA”) as part of Bill C-27, the Digital Charter Implementation Act, 2022, which was Canada’s first attempt at regulating AI specifically. AIDA’s purpose was to ensure the safe and responsible design, development, and use of AI technologies by private sector entities, and aimed to balance innovation with ethical and safety standards. AIDA included a variety of requirements for high-impact AI systems, such as certain biometric systems used for identification and inference. AIDA proposed mandatory requirements for high-impact AI systems, including risk assessments, transparency obligations, and incident reporting. Penalties for non-compliance were steep, with fines reaching up to $25 million CAD or 5% of global revenue. Despite certain criticisms of AIDA, such as its vague scope and requirements, lack of meaningful public consultation, and exclusion of government use of AI, AIDA represented a promising step in Canada’s journey to regulate high-impact AI systems. DOI: 10.5281/zenodo.17328882 71
Chapter 6: AI Surveillance, Privacy, and Human Rights PART II | SAIER Vol. 7 | Nov 2025 However, Bill C-27 never made it to a final vote. When Parliament was prorogued in early 2025, AIDA was dropped from the legislative agenda. With no clear timeline for the return of AI-specific regulation, Canada remains without a dedicated federal law to address the growing influence of AI on Canadians, and the legal framework meant to govern these technologies remains a work in progress. The Un-Clearview of Biometrics and Canadian Privacy Law Protections In the absence of AI-specific legislation, Canada has relied on other frameworks to regulate risks related to AI biometric technologies. The recent Clearview AI (“Clearview”) cases are examples that tested Canadian privacy laws. Clearview developed a facial recognition AI tool built from over three billion images scraped from public websites, including social media. Law enforcement agencies and other users accessed the Clearview tool to match uploaded images with biometric identifiers, including those of Canadians. Clearview claimed it did not need consent from individuals to collect their images because they were publicly available and therefore exempt from the requirement to obtain individuals’ consent to collect, use, or disclose their personal information. However, privacy commissioners from Canada, British Columbia, Alberta, and Québec jointly investigated and found that collecting identifying biometrics, such as images from public websites, and then using them for an unrelated purpose, such as training AI systems, without individuals’ consent, does not fall under the “publicly available” exceptions under Canadian privacy laws. Courts in British Columbia and Alberta upheld the privacy commissioners’ conclusions that posting images on social media does not exempt them from consent requirements under the British Columbia Act and Regulations, and Alberta Act and Regulation, simply because they are publicly available. However, in contrast to the British Columbia decision, the Alberta Court of King’s Bench ruled that the Alberta definition of “publicly available information” breached the Charter right to freedom of expression and is unconstitutional. This ruling may create an uneven privacy law landscape in Canada and suggests that collecting images from certain public sources, such as social media, for use in AI systems may be permissible without consent in some circumstances. In arriving at this decision, the Court stated that, “The internet today is very different than it was in 2003” when the applicable sections of the Alberta Regulation were adopted. The same observation applies to AI: the technology is advancing dramatically, and while many Canadian laws remain anchored in a pre-AI era, they are now being stretched to address risks that AI presents. DOI: 10.5281/zenodo.17328882 72
Chapter 7: Environmental Impact of AI PART II | SAIER Vol. 7 | Nov 2025 2024, the World Meteorological Organization noted that 2023 was the driest year for global rivers in over three decades, and swathes of the world are experiencing more frequent and more severe droughts. Unfortunately, data centres are often geographically clustered in the very areas that are experiencing high levels of water stress. Part of the solution is innovation and collaboration. Data centre cooling methods with lower water withdrawals and lower average water loss can mitigate some of this stress. Some cloud service providers have experimented with novel solutions like underwater data centers that solve both the cooling problem and component corrosion problem. It's important also in the mad rush to attract AI infrastructure for regulators to ask whether proposed projects will have enough water to sustain hyperscale data centres and the basic needs of the people living there. The environmental stress that occurs can also amplify conflict, with drier climate prolonging civil unrest. Santiago, Chile, is home to 16 of the country’s 22 data centres, and is seeing escalating local resistance as the country experiences its longest and most intense megadrought. The US is similarly seeing localized protests against data centre projects. While we are seeing more concerted efforts both toward technical solutions and towards transparency, there remain gaps in the availability of data to understand the full lifecycle impact of AI in 2025. Industry players are signaling commitment through sustainability reporting, although they remain reticent to share data on their energy use to independent researchers for proprietary reasons. A full lifecycle assessment of AI’s environmental impact remains a grand challenge. As the overview above shows, the factors contributing to AI-linked emissions span vast and intricate supply chains. Building a truly comprehensive picture would require granular data from suppliers and service providers at every stage. Yet, understanding AI’s cumulative value to the global economy cannot happen in isolation: it intersects with broader megatrends such as economic volatility, resource scarcity, and shifting patterns of industrial growth. About the Author Trisha Ray is an associate director and resident fellow at the Atlantic Council’s GeoTech Center, where she leads the AI portfolio. Her research lies at the intersection of geopolitical and security trends in relation to emerging technologies. Prior to this, Ray was a fellow and deputy director at the Center for Security, Strategy and Technology at the Observer Research Foundation in India. Cite this Article Ray, T. (2025) Measuring the Environmental Impact of the AI Supply Chain. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 78-79. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 79
Chapter 7: Environmental Impact of AI PART II | SAIER Vol. 7 | Nov 2025 7.3 Policies Centring AI’s Resource Consumption By Priscila Chaves Martínez , Independent Researcher and Consultant In 2025, AI’s scaling ambitions raised concerns across the entire supply chain: data centre power demand is projected to double up to ~945 TWh by 2030, with AI as the primary driver. This tracks a fourteen-year pattern in which training compute has grown 4-5 times per year since 2010. The ticket to get there is estimated at $490 million USD, 310 million kWh of electricity, 140,000 tons of CO₂, and 750 million liters of cooling water, enough energy to power a town of 4,000 people, emitting as much as a Boeing aircraft flying non-stop over three years, and water to fill 300 Olympic-sized swimming pools. At the core of this challenge is transparency, as environmental data from major industry players remain unreliable and non-comparable: their methods are opaque and disclosure has regressed since 2022, creating misinformation by omission. Accountability is long overdue: industry must disclose comparable, auditable, life-cycle impact data for the entire AI supply chain if sustainability is the goal. Why 2025 Matters This year evidenced how power, policy, and profit organize flows of matter, energy, and labour. Crawford and Joler mapped the anatomy of the AI lifecycle: extraction, manufacturing, energy and water use, hidden labour, and e-waste, arranged by capital and colonialism. With this in mind, two shifts made this year pivotal. First, AI became a subject of critical importance for infrastructure policy decisions. The US Department of Energy warned that load growth from AI cannot be met with business-as-usual rules, with the grid incapable of absorbing AI growth without “radical change” in interconnection and new firm and flexible supply. The Federal Energy Regulatory Commission opened proceedings on co-locating data centres at power plants. The politics of scale moved from boardrooms to congressional sessions. In Europe, under the EU Energy Efficiency Directive, data centre operators were required to report energy, water, and efficiency metrics to a new registry, with the Commission’s First Technical Report of 2024 data published in July 2025, and a public dashboard in the works. The policy doesn’t solve for full lifecycle impacts, but it makes the footprint measurable, which is the precondition for accountability. Second, the AI supply chain came under public scrutiny. Investigations and strategic litigation put AI’s energy, water, labour, and e-waste impacts on the record. In Mexico, Chile and Spain, journalists and civic groups mapped and denounced the intricate dynamics of power, opaqueness and corruption that let hyperscalers Microsoft, Amazon and Google skip environmental impact reports during droughts. DOI: 10.5281/zenodo.17328882 80
Chapter 7: Environmental Impact of AI PART II | SAIER Vol. 7 | Nov 2025 While environmental data is obfuscated, what has remained clear is who is bearing the costs of the AI hype. We are now aware that mining just one tonne of rare-earth minerals (critical components inside our mobile phones) can generate about 2,000 tonnes of toxic waste; displace communities; contaminate water and farmland with acutely toxic tailings linked to deformities; and drive child labour and human-rights abuses at mining sites from Inner Mongolia to the DRC, not to mention violent conflicts with Lickanantay communities over water in the Atacama Desert. In the Global North, e-waste is growing five times faster than documented recycling, with roughly 95% of it processed in cities like Delhi and Accra. Health studies in Ghana and Nigeria link proximity to e-waste sites to infant mortality. Content moderation and data labelling became environmental-justice issues with Kenya’s landmark Meta case, creating a precedent for Ghanaian moderators to file investigations and lawsuits against Meta in 2025 over severe psychological harms. Non-Negotiables in 2026+ Time is up. The latest records show the global temperature between April and September 2025 kept the 12-month average at or above 1.5°C; 2024 was the warmest year on record; ocean heat content hit a new high and marine heatwaves persisted into mid-2025. The impacts are immediate: roughly 295 million people faced high levels of acute food insecurity in 2024, with new hotspot alerts for 2025. Conflict, economic shocks, and climate extremes disrupted supply chains and forcibly displaced 123.2 million by end-2024. Of these, 83.4 million were internally displaced, doubling compared to 2018. The coming years will test whether we can align model ambition with planetary limits. We must distance ourselves from the manipulative rhetoric from industry leaders claiming their AI will be the one “fixing the climate.” Instead, our communities have the capacity to orchestrate collective action, demand for radical accountability and get governance right: “AI infrastructure” can mean jobs, cleaner grids, and resilient watersheds. Anything less is externalizing costs onto the same marginalized communities that mine our devices, go thirsty so that our racks are kept cool, and breathe our waste. If you’re building or buying AI in 2026, you inherit the full anatomy: extraction, fabrication, operation, disposal. Before approving any flashy AI project, start by answering these questions: Where is it? Who owns it? What does it consume (hour by hour, basin by basin)? Who carries the residuals? Then measure it, set compute, carbon and water budgets, and report in your scope 3 emissions. Be accountable for the real cost of your AI supply chain. DOI: 10.5281/zenodo.17328882 81
Chapter 7: Environmental Impact of AI PART II | SAIER Vol. 7 | Nov 2025 About the Author Priscila Chaves is a practitioner-researcher in AI transformation and responsible innovation with 18+ years’ experience across seven continents. She has held senior roles at IBM and Cargill, advised governments in Eastern Africa and Latin America on AI governance, and conducted Antarctic research on climate impacts. Her work examines how technology reshapes trust, communities and governance. Her degrees are in business, technology, and AI ethics from the Universidad de Costa Rica, NYU Stern, Cambridge, and Oxford Saïd (in progress). Cite this Article Chaves Martínez, P. (2025) Policies Centring AI’s Resource Consumption. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 80-82. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 82
State of AI Ethics Report | Volume 7 November 2025 PART III: SECTORAL APPLICATIONS Chapter 8: Healthcare AI – When Algorithms Meet Patient Care Chapter 9: AI in Education – Tools, Policies, and Institutional Change Chapter 10: AI and Labour Justice Chapter 11: AI in Arts, Culture, and Media DOI: 10.5281/zenodo.17328882 83
PART III: SECTORAL APPLICATIONS SAIER Vol. 7 | Nov 2025 Chapter 8: Healthcare AI – When Algorithms Meet Patient Care 8.1 Learning to Diagnose: How AI’s Digital Twins Are Redefining Patient Care By Rosa E. Martín-Peña, Centre for Ethics and Law in the Life Sciences (CELLS) at Leibniz University Hannover 8.2 Medical Trade Unions and Professional Bodies are Taking Back Control and Oversight of AI in Healthcare By Zoya Yasmine, University of Oxford DOI: 10.5281/zenodo.17328882 84
Chapter 8: Healthcare AI PART III | SAIER Vol. 7 | Nov 2025 8.1 Learning to Diagnose: How AI’s Digital Twins Are Redefining Patient Care By Rosa E. Martín-Peña , Centre for Ethics and Law in the Life Sciences (CELLS) at Leibniz University Hannover After the COVID-19 pandemic, it became clear that medicine’s static view of the patient was no longer enough. While symptoms unfold in time and according to context, we are mostly still captured as static photographs: a single blood-pressure reading, an X-ray snapshot, a few seconds of ECG or pulmonary data. Between those moments, life moves, but the clinic does not. Diagnosis remains a still image of a moving body. Medicine has had to learn to diagnose anew. The recent advances in AI do not stem only from technological ambition but from a deeper necessity: the need to face uncertainty. What drives this new wave of clinical AI is precisely what eludes traditional practice; the fragments of information that cannot be captured as a continuous flow. Each heartbeat, each chemical fluctuation, each subtle change in breath contains a story that resists static representation. AI, and especially the rise of digital twins, attempts to weave those fragments into a living model. By 2025, digital twins had moved from theory to clinical pilots, marking a shift in how medicine imagines the body. Originating in aerospace engineering, where virtual replicas of machines were used to monitor performance and anticipate failure, the concept has been reimagined for healthcare. A digital twin is an AI-driven model that continuously integrates genomic, physiological, behavioral, and environmental data to create an adaptive representation of the patient. Early pilots in cardiology, oncology, and orthopedics now use these twins to simulate disease progression and guide treatment. In neurology and mental health, researchers are exploring how dynamic biomarkers, such as neural oscillations or pain signatures, might be modelled over time. These systems offer a dynamic form of seeing: continuously updated mirrors of patients that promise to make care more predictive and personalized. To compute, every dataset must be coded, categorized, and cleaned. In digital-twin medicine, the messy language of the body is translated into data streams and labels. What cannot be encoded, lived experience, quietly disappears. This extends medicine’s reach but also narrows its meaning. When the model’s predictions diverge from the patient’s story, it is often the patient who is doubted. The twin’s precision becomes a new kind of authority: statistical, opaque, and indifferent to uncertainty. Reducing uncertainty to discrete categories may look like progress, but it can also be dangerous. A model that tracks every vital sign can still miss what matters most: context. The same fluctuation may signal harmless fatigue in one person and relapse in another. Systems designed to “reduce variability” often treat the human element as the problem to be solved. Yet the inconsistency of human judgment is what allows for empathy, revision, and attention to what doesn’t fit. DOI: 10.5281/zenodo.17328882 85
Chapter 8: Healthcare AI PART III | SAIER Vol. 7 | Nov 2025 Continuous observation also transforms into continuous supervision. A physician cannot follow a patient twenty-four hours a day; a digital twin can. This vigilance can detect deterioration early, prevent hospitalizations, and guide treatment remotely, but it can also redefine care as control. Health becomes a state to be optimized, and the patient’s privacy quietly erodes. Traditional medicine has always relied on feedback. A clinician observes, intervenes, listens, and adjusts. The diagnosis evolves through conversation and correction. Digital twins, in theory, promise to reinvigorate that reciprocity: they continuously update with new data from the patient. Yet this feedback remains technical, not epistemic. The system adapts its parameters, but it does not learn from narratives or disagreement. Its predictions still flow in one direction, from model to clinician to patient, without true dialogue or correction. The patient’s lived story rarely alters the assumptions the model was built on. The epistemic circle that sustains medical reasoning remains broken. In 2025, the promise and the peril of this technology are both becoming clear. Clinical pilots in Europe and North America have shown that digital twins can detect subtle physiological changes earlier than traditional monitoring, yet they also expose deep challenges of interoperability, accountability, and patient agency. What happens when a patient’s virtual body begins to contradict their lived experience? Who has the authority to decide which version of the body counts as “true”? For now, digital twins still learn mostly from data points. The evolving stories of patients (their sensations, doubts, and interpretations of illness) rarely enter the model’s purview. This absence is not accidental but structural: current systems are built around measurable signals, not lived meanings. Integrating subjective experience would require new semantic infrastructures, ethical safeguards, and an epistemic shift in how medicine values patient knowledge. The next phase of development will depend on whether healthcare systems can take that step. The real innovation will not be more accurate prediction, but more responsive dialogue: systems capable of learning from error, disagreement, and the voices of patients themselves. If medicine is, at its heart, an art of uncertainty, then the future of AI in healthcare will depend on whether doctor and machine can truly learn to diagnose together, not as rivals in precision, but as partners in listening. About the Author Rosa E. Martín-Peña is a postdoctoral researcher at the Centre for Ethics and Law in the Life Sciences (CELLS) at Leibniz University Hannover, where she leads the ethics of AI-based decision systems within CAIMed, the Lower Saxony Center for AI and Causal Methods in Medicine. Her work explores the epistemic and ethical dimensions of artificial intelligence and medical data, with a particular focus on uncertainty, responsibility, and co-adaptive design in clinical AI systems. Cite this Article Martín-Peña, R. E. (2025) Learning to Diagnose: How AI’s Digital Twins Are Redefining Patient Care. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 85-86. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 86
Chapter 8: Healthcare AI PART III | SAIER Vol. 7 | Nov 2025 8.2 Medical Trade Unions and Professional Bodies are Taking Back Control and Oversight of AI in Healthcare By Zoya Yasmine , University of Oxford Medicine is a discipline that has long been grounded in ethics, regulation, and professional standards which govern clinician conduct, patient care, and the development of new technologies. Relative to others, these established frameworks position the medical community well to critically assess the development and deployment of AI. In countries such as the UK, healthcare professionals have used this foundation to demand greater transparency and control over AI in healthcare. Below, I point to two examples from 2025 in which the UK’s British Medical Association (BMA), the trade union and professional body for doctors and medical students, are actively challenging the deployment of untested technologies and the use of patient data for AI training. The examples discussed here are not intended to assign blame or criticise the development of medical AI by many well-intentioned researchers. Instead, they highlight how trade unions can serve as early warning mechanisms and maintain a principled commitment to the core values of medical practice. These sites of discussion are a ripe opportunity to guide the reform and refinement of regulation, law, guidelines, and policy that govern medical AI. I struggled to find other cases of resistance from global communities that occurred in 2025 (although I did find one in the US), which may reflect the uniquely interventionist and vocal role of the BMA in the UK. Example 1: Challenges to “Untried and Untested” Medical AI In September 2025, a motion passed by the BMA supported its members who refuse to use “untried and untested” AI systems. The motion was likely motivated by the UK Government’s “pro-innovation” and “digital first” agenda, which is pushing efforts to integrate AI in healthcare settings. The BMA’s explicit backing of doctors who reject untested AI systems sends a strong message that patient safety and professional accountability take precedence over the uncritical adoption of technology. It remains the duty of healthcare professionals to exercise their professional judgement when treating patients using technology. When AI undermines the ability to do this properly, it compromises their duty of care towards patients. Although clinicians are, in theory, expected to provide oversight over AI decisions, their ability to act as a “human-in-the-loop” is often limited or obstructed by the way these technologies are deployed, for example, with opaque reasoning or insufficient control. The BMA’s support represents a collective call from the medical community for stronger testing and thoughtful implementation of AI in healthcare to protect both patients and healthcare professionals. DOI: 10.5281/zenodo.17328882 87
Chapter 8: Healthcare AI PART III | SAIER Vol. 7 | Nov 2025 Example 2: Challenges to the Use of Patient Data in Training Medical AI In May 2025, a “groundbreaking AI initiative” involving a training dataset of 57 million general practice (GP) health records was announced between researchers at the University College London and King’s College London. The AI model, known as “Foresight,” aimed to predict future patient outcomes based on individuals’ medical histories. However, shortly after its announcement, NHS England paused the project following concerns raised by the BMA and the Royal College of General Practitioners (RCGP), the UK’s professional body for GPs, through their Joint GP IT Committee. The groups demanded greater transparency over how patient data was accessed and used for training Foresight. In the UK, there are limited circumstances in which patient data may be used without explicit consent. Whether the Foresight initiative falls within these legal exceptions is currently under investigation. The concerns expressed by the BMA and the RCGP underscore how the development of medical AI must be guided by transparency and trust in how data is used, not merely legal compliance. If patients lose confidence in how their data is used, they may withdraw from healthcare services or withhold information which could have serious consequences, especially in the context of infectious diseases. The actions of the BMA and the RCGP reaffirm that patient trust remains a core value in medical practice and that it might be time to re-evaluate the legal and ethical requirements for researchers and companies who access health data for research without patient consent. Reflections These two examples show the active role that the BMA and the RCGP are playing in shaping a more critical approach to the development and deployment of AI in the UK’s healthcare system. To build on this, we must listen to clinicians who raise legitimate concerns about “untried” and “untested” AI technologies, and develop clearer rules and mechanisms to govern when, how, and who can use patient data for AI research. Advancing these steps will depend on the engagement of the BMA and healthcare professionals who prioritise the values of trust, transparency, patient safety, and integrity which define medical practice. About the Author Zoya Yasmine is a DPhil student at the University of Oxford. Her research explores the intersection between medical AI, ethics, and law with a specific focus on intellectual property and data protection. Cite this Article Yasmine, S. (2025) Medical Trade Unions and Professional Bodies are Taking Back Control and Oversight of AI in Healthcare. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 87-88. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 88
PART III: SECTORAL APPLICATIONS SAIER Vol. 7 | Nov 2025 Chapter 10: AI and Labour Justice 10.1 Restoring Employee Trust in AI By Dr. Elizabeth M. Adams, Minnesota Responsible AI Institute 10.2 AI in Oil and Gas: The Case of Alberta By Ryan Burns, University of Washington Bothell; and Eliot Tretter, University of Calgary DOI: 10.5281/zenodo.17328882 95
Chapter 10: AI and Labour Justice PART III | SAIER Vol. 7 | Nov 2025 10.1 Restoring Employee Trust in AI By Dr. Elizabeth M. Adams, Minnesota Responsible AI Institute The Missing Voice in AI Adoption In a workshop I facilitated on Responsible AI (RAI), I asked employees, “How many of you are afraid of AI?” Of the nearly fifty participants, most raised their hands. They were concerned about job security and how their roles would be affected. Many were wondering if they can trust that their organizations will invest in upskilling, as they are required to adopt and use AI tools. Others were concerned about the decision-making process of leaders and AI systems. They were wrestling with whether decisions are fair, especially if used to determine performance evaluations. As highlighted in a recent HR Drive article, employees report feeling overwhelmed by the demands placed on them by leaders. They want consistency, not gaps that create fractures in an already challenging work environment. A critical question thus remains unanswered: Who is shaping the future of work? When addressing AI adoption challenges in organizations, the foundational issue of employee stakeholder engagement is still overlooked, especially when adoption is driven by fear, urgency, or executive assumptions related to competitive advantage. In the rush to deploy and adopt AI, many organizations sideline the very people whose insights could safeguard against bias, job loss, and surveillance: their employees. The Trust Gap A growing number of US employees believe that AI could improve fairness and efficiency, and they also say that clear AI practices would increase their trust in its deployment. However, one of the most troubling trends is the belief that surveillance over engagement is the answer. “Helicopter managers”, as they are often referred to, closely manage daily tasks and monitor employee activity through workplace surveillance technologies. When organizations rush to implement AI tools, they often assume these technologies will outperform employees, reduce costs, and create efficiencies. However, these assumptions can lead to employee disillusionment and a deep erosion of trust. At the Minnesota Responsible AI Institute, we have seen firsthand how limited expertise in AI procurement and development can result in tools that perpetuate bias, misalign with organizational values, and alienate the very people they need to support AI adoption. Centring the Employee Stakeholder for Alignment Organizations that invite employees into the AI adoption process foster deeper trust and more sustainable innovation. When employees are engaged early and often, trust becomes a strategic advantage. Moreover, trust enables the organization to shift from DOI: 10.5281/zenodo.17328882 96
Chapter 10: AI and Labour Justice PART III | SAIER Vol. 7 | Nov 2025 chaos to making better decisions that align its AI vision with its core values, which in turn reduces internal conflict and fosters long-term strength. Restoring employee trust in AI requires more than compliance checklists. It demands a cultural shift: one that recognizes employees not as passive recipients of change, but as active partners in innovation. By prioritizing a commitment to Responsible AI, organizations can foster a more holistic approach that enables employees across all levels to engage meaningfully and purposefully. People Shape the Future of Work One potential solution for increasing employee stakeholder trust is the application of Design Science Research (DSR). De Leoz and Petter (2018) emphasize that DSR is a distinct research paradigm within the field of Information Systems. DSR offers an approach for building employee trust that incorporates centering the employee voice when designing AI systems, processes, or procedures. When employees’ voices are actively engaged with RAI practices, organizations gain a more comprehensive understanding of societal implications and ethical considerations (De Leoz & Petter, 2018; Mayer et al., 2020). A process that can directly strengthen trust. As Mayer et al. (2020) suggest, when aligning responsible innovation with societal considerations, and those informed by employees' lived experiences, organizations can foster the development of ethically sound and socially beneficial AI systems. The future of work and AI hinges on whether organizations prioritize centering the people who are shaping the future of work, their employees. Trust grows when AI strategies align with core values and are supported by tools that demonstrate a commitment to greater employee stakeholder engagement. The future of work is about people. Employees are the people. When employees are invited to shape that future, we all benefit. About the Author Dr. Elizabeth M. Adams, founder of the Minnesota Responsible AI Institute, is a strategist helping organizations align culture, leadership, workforce development, and employee engagement in AI integration and organizational readiness. She transforms complex ethical frameworks into actionable strategies that empower leaders to steward AI responsibly across industries. A sought-after global speaker, Dr. Adams champions ethical, human-centered AI from national platforms to international policy forums, advancing Responsible AI through scalable, values-driven leadership. Cite this Article Adams, E.M. (2025) Restoring Employee Trust in AI. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 96-97. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 97
Chapter 10: AI and Labour Justice PART III | SAIER Vol. 7 | Nov 2025 10.2 AI in Oil and Gas: The Case of Alberta By Ryan Burns , University of Washington Bothell; and Eliot Tretter, University of Calgary At first blush, AI and petroleum extraction might appear to be on opposite ends of the human experience: the former is based on data harvesting and computation, and the latter's natural source material exists on a geological timescale predating humans entirely. However, there is growing overlap between the two: AI is becoming increasingly suffused in the work of petroleum extraction. It can be found in autonomous vehicles, drones and robotics, data analytics, automation, and so on. This has important ramifications for labour justice that are often overlooked in scholarly and popular conversations about AI's labour impacts. Our research focuses on how AI shifts the geographies of petroleum extractive labour, and we look specifically at the case of Alberta, the dominant oil and gas producer in Canada. Increasing automation allows greater centralization of labour in places far from the oilpatch. In Alberta, this often means that work being done in the oil sands region of Wood Buffalo and Fort McMurray being relocated to Calgary, 450 miles away, or Edmonton, 250 miles away. In these cities, workers are able to monitor incoming data streams from sensors, control drones and robotics doing inspections, and monitor autonomous trucks and pumpjacks. The consequence of this is that fewer people need to commute to the oil sands region. A senior executive for Imperial Oil recently said, "With our fully automated fleet, we’re improving safety by removing the worker from the hazard while offering efficiencies and work execution"; and later, "We estimate that [Imperial's 'four-legged robot' Spot] can conduct almost 70% of some operator rounds, allowing us to reallocate operator and maintenance resources to higher value work", presumably in these cities. Fewer workers are traveling to Wood Buffalo and Fort McMurray, and this may be causing a "de-fielding" of the oilpatch. From 2018 to 2024, airport traffic to the main airport decreased by 40.6%, and concurrently, overall employment in Wood Buffalo fell 11.3%. Over this same time period, employment in Calgary in mining, quarrying, and oil and gas extraction remained steady at around 6.5%, while oil and gas production rates have consistently increased to reach record highs in 2024. What does any of this have to do with labour justice? We are observing two important effects of these geographic shifts. The first is that on-site work may tend to fall to precarious, short-term, often freelance contracts, mediated by digital platforms like RigUp and Rigzone. Many social scientists have documented the ways that such precaritization reduces labour's ability to organize and collectively bargain for protections and fair wages. It also removes these workers from a firm's formal employment roster, allowing the firm to forego offering certain benefits such as private health insurance, a retirement package, or disability insurance. DOI: 10.5281/zenodo.17328882 98
Chapter 10: AI and Labour Justice PART III | SAIER Vol. 7 | Nov 2025 The second effect is that, with the reduced number of travelers to the oil sands, the support service industry would experience declines in their client base. With fewer clients to service, economic activity is hurt. As reflected in Government of Canada statistics showing that in 2024 there were "increases in employment in every economic region in Alberta, except for Wood Buffalo-Cold Lake," which declined by about 1.7% of the entire regional labour base. An important note is that these trends will likely disproportionately impact First Nations communities, as they comprise a large percentage of the service industry in the region. Indigenous-owned businesses and communities are likely to suffer the worst of the impacts of these shifting geographies of work. While it is not the objective of this chapter, our research suggests that these two effects should further be more central in discussions about a just transition to post-carbon economies, where AI similarly plays an outsized role. Together, the two effects we observe bear striking similarity to similar geographic industrial shifts in the US, such as the decline of coal production in the Appalachia region. One could suggest that this isn't "AI's fault." We would agree, if what is meant is that the technology itself didn't instigate the industrial shifts. However, technology is never neutral, as it is embedded within institutional, political, and social contexts. In the Alberta case, such contexts include pressures to confront global climate change, global decarbonization, and a foreshadowed slowdown of carbon-based energy consumption. In other words, what we see here is a socio-technical transformation, where one cannot separate the technology from society, which has strong implications for the social geographies of job losses. In this sense, whether it is "AI's fault" is beside the actual point that AI is advancing problematic labour relations in concert with software engineers, Chief Technology Officers, policymakers, corporate leaders, and venture capitalists. About the Authors Dr. Burns is Affiliate Professor at University of Washington Bothell whose work explores the social and political implications of emerging technologies. He is a Global Ambassador for the Global Council on Responsible AI, and a Member of the United Nations University Global AI Network. Dr. Tretter is Associate Professor at the University of Calgary. He is author of Shadows of a Sunbelt City (2016), and his latest book project, tentatively titled Petrocity, explores the complex effects of hydrocarbon extraction on Calgary's urbanization. He and Dr. Burns lead the “Digitizing Carbon Capitalism” project, which examines how the digital economy transforms labour in the extractive industries. Cite this Article Burns, R. & Tretter, E. (2025) AI in Oil and Gas: The case of Alberta. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 98-99. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 99
PART III: SECTORAL APPLICATIONS SAIER Vol. 7 | Nov 2025 Chapter 11: AI in Arts, Culture, and Media 11.1 Media Jobs are Canaries in the AI Automation Coal Mine By Katrina Ingram, Ethically Aligned AI 11.2 2025 Marks a New Era for Canadian Performers: The First Collective Agreements with AI Protections By Anna Sikorski, ACTRA Montreal; and Kent Sikstrom, ACTRA National 11.3 The Ursula Exchange By Amanda Silvera, Independent Voice Actor, AI Ethics in Art and Entertainment DOI: 10.5281/zenodo.17328882 100
Chapter 11: AI in Arts, Culture, and Media PART III | SAIER Vol. 7 | Nov 2025 11.1 Media Jobs are Canaries in the AI Automation Coal Mine By Katrina Ingram, Ethically Aligned AI Legacy media has been in decline for decades. First, social media gutted ad-driven business models, creating pressure to cut production costs. At the same time, there was an explosion of digital offerings, making the quest for audience attention even more challenging. Now there’s AI to exacerbate the trend. “Sarah,” an AI DJ, which launched on Edmonton’s SONIC radio this summer, is part of an emerging pattern to automate on-air talent. It started in 2023 with AI Ashley, cloned from the voice of DJ Ashley Elzinga. Elizinga opted into this experiment, but the hosts at Polish station, Radio Krakow, weren’t so fortunate; management replaced all of them before backtracking on the failed experiment. Their failure is a cautionary tale, but it’s not deterring stations from choosing AI as it becomes indistinguishable from human voices. A 2025 report by Tomlinson et al. lists broadcasters amongst the most at risk occupations. Source: Tomlinson et al. (2025) - Working with AI: Measuring the Applicability of Generative AI to Occupations Who is ‘Thy’? An upbeat young female voice took to the airwaves on the Australian Radio Network (ARN) with a daily four hour long music show, but ARN did not disclose their new “host” as AI. It took six months before some listeners began to wonder, who is “Thy”? Blogger Stephanie Coombes broke the story and people were, understandably, upset at being deceived. However, it wasn’t the on-air presentation that was lacking. It was the absence of a social media presence for someone supposedly in their 20s that gave “Thy” away. It’s DOI: 10.5281/zenodo.17328882 101
Chapter 11: AI in Arts, Culture, and Media PART III | SAIER Vol. 7 | Nov 2025 disquieting to consider the lengths of the deception that ARN would have needed to go to had they wanted to provide air-tight cover. The incident raises questions about the obligations of media organizations to be truthful and transparent with audiences. It also raises the question: should disclosure of AI be mandated by regulation, a stance that only China has recently implemented? There are laws that demand truth in advertising which might also be extended to truth in representation for public figures, like media personalities. This could be enacted through station licensing. In addition to the deception itself, there’s also the issue of agency. “Thy” was modelled on an unnamed Asian-Australian female ARN staffer. Teresa Lim of the Australian voice actors called out ARN for taking away jobs from an already struggling minority group. Coombes noted most ARN on-air talent were white. “Thy” was their “diversity hire”… except “Thy” was not human! The Ethics of AI Voices There are now many options such as Eleven Labs, PlayAI or Futuri widely available for creating AI voices. The ethical implications of “owning” someone's voice as part of their work product are immense. This cloned voice could, quite literally, be made to say anything - endorsing products or opinions that might not align with one’s values. Media organizations have traditionally held ethical standards as trusted sources of information. Tying station licensing to responsible use might be one lever for better governance. Stations can also choose off the shelf voices, trained using questionably acquired data scraped from the internet. While human voices are made economically unviable, AI voices are marketed as cost effective solutions. AI voices used in ads and audio books are now coming into the higher profile space of on-air talent. However, a radio host is more than a voice, they’re a source of human connection. Assigning this role to a bot not only has economic consequences, but it also degrades the role. We’re Not Replacing Jobs The mantra across most stations using AI is that they are not replacing humans, and are only using it to fill roles in timeslots without human hosts, such as overnight shows. Overnight shows were traditionally where new talent starts, but it has now largely been abandoned because of staffing costs. However, SONIC’s “Sarah” and ARN’s “Thy” suggest all timeslots are up for grabs in the quest to reduce costs. In fact, having no human hosts is the business model for Inception Point, which plans to release thousands of AI generated podcasts, made for just a dollar an episode. CEO Jeanine Wright told the Hollywood Reporter “We believe that in the near future half the people on the planet will be AI, and we are the company that’s bringing those people to life”, which makes one wonder how Wright defines a person or life. Ironically, while DOI: 10.5281/zenodo.17328882 102
Chapter 11: AI in Arts, Culture, and Media PART III | SAIER Vol. 7 | Nov 2025 humans are eliminated from production, they’re still very much needed for consumption to support ad-driven business models. There is some hope. New data suggests a listener backlash with 47% less likely to listen and 28% much less likely to listen to AI voices. Even “AI Ashley” was “retired” this year. Is this a preference for human voices? A protest against AI automation? Is it enough to change course? AI voice technology coupled with cost cutting imperatives make broadcasters canaries in the AI automation coalmine. The idea that ‘AI will not take your job, but a person using AI will’ isn’t holding true for the world of radio so far, and may it stay that way. About the Author Katrina Ingram is the Founder and CEO of Ethically Aligned AI, a company focused on advancing Responsible AI literacy. A seasoned executive, Katrina has over two decades of experience running both not for profit and corporate organizations in the technology and media sectors. She was named to the 100 Brilliant Women in AI Ethics. Katrina developed Canada’s first micro-credential in AI Ethics in partnership with Athabasca University and has served as the City of Edmonton’s Data Ethics Advisor. Cite this Article Ingram, K. (2025) Media Jobs are Canaries in the AI Automation Coal Mine. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions,. pp. 101-103. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 103
Chapter 11: AI in Arts, Culture, and Media PART III | SAIER Vol. 7 | Nov 2025 11.2 2025 Marks a New Era for Canadian Performers: The First Collective Agreements with AI Protections By Anna Sikorski, ACTRA Montreal; and Kent Sikstrom, ACTRA National The Alliance of Canadian Cinema, Television and Radio Artists (ACTRA) represents performers working in English recorded media across Canada. ACTRA groups across Canada are aligning on AI policy, bargaining, and advocacy to ensure consistent protections for performers coast to coast. This national coordination helps keep performers’ rights front and centre as AI reshapes the industry. 2025 marked the start of a new era for ACTRA, both the IPA (Independent Production Agreement) and the BCMPA (British Columbia’s Master Production Agreement) included newly negotiated AI provisions. As part of this new era, productions may use AI technologies to create and use digital replicas of performers’ likeness or voice while respecting three core principles, or the Three C’s: consent, compensation and control. ● Consent: Performers must consent to the use of AI for the creation and use of their likeness (or part thereof) or voice. ● Compensation: Performers must be paid for all uses, including their participation in the creation of any digital assets. Performers must also be compensated for all days they would have worked had the digital replica not been created and used. ● Control: The employer must guarantee that the Performer data will be safely stored and tracked to ensure no uncontracted or nonconsensual use occurs. The Agreements also have provisions for the potential use of “Synthetic Performers.” As AI-generated assets become more sophisticated, this language establishes parameters and a foundation for future bargaining, recognizing that any digital asset that has the potential to replace human performance constitutes labour that deserves fair compensation. While once an abstract dystopian possibility, this new reality has recently reared its little semblance of a head with the arrival of AI actress Tilly Norwood. Already, EQUITY UK has threatened mass direct action against Tilly’s creator, Particle6, since the likeness and mannerisms of at least one of their members seems to have been used in the creation of the AI-generated asset without her consent. Collective agreement protections are limited in scope to jurisdictions and union contracts. What is needed are substantive protections in the form of policy and legislation. Unions are doing their best to safeguard performers, but they cannot bear the weight of this issue alone. The Writers Guild of America and Screen Actors Guild - American Federation of Television and Radio Artists (SAG-AFTRA) needed to strike to achieve AI protections; and, DOI: 10.5281/zenodo.17328882 104
PART IV: EMERGING TECHNOLOGIES SAIER Vol. 7 | Nov 2025 Chapter 12: Military AI and Autonomous Weapons 12.1 A Minute Before Escalation: Algorithmic Power and the New Military-Industrial Complex By Ayaz Syed, The Dais, Toronto Metropolitan University 12.2 Civil Society’s Responses to the Militarization of AI By Kirthi Jayakumar, civitatem resolutions DOI: 10.5281/zenodo.17328882 111
Chapter 12: Military AI and Autonomous Weapons PART IV | SAIER Vol. 7 | Nov 2025 12.1 A Minute Before Escalation: Algorithmic Power and the New Military-Industrial Complex By Ayaz Syed , The Dais The ethical arc of modern military decision making (from moments of restraint during false nuclear alerts to contentious battlefield decisions over life and death) highlights how individual judgment in complex, high-stakes situations determine whether or not violence escalates. Such legacies frame the problem for military AI in 2025, where human judgment is increasingly displaced by algorithmic systems. The stakes are manifest globally. We are quickly reaching an inflection point as the line has shifted from theoretical debates to actualized deployments of military decision support systems and lethal autonomous weapon systems alongside intensifying geopolitical competition to dominate the computational infrastructure space enabling such systems. Ongoing conflicts in 2025 continue the trend of Lethal Autonomous Weapons Systems (LAWS), commonly called “killer robots,” leveraging AI-targeting modalities. With it, critical failures are cascading across sociotechnical domains. After confirmation of early 2023 reports of Israeli forces utilising an AI tool called Lavender to generate mass kill lists, Microsoft terminated cloud and AI services for Israeli Intelligence Unit 8200, establishing a red line for corporate engagement in certain military AI practices. Yet, reports also indicate that Unit 8200 prepared to migrate surveillance data to Amazon Web Services. As it stands, corporate self-regulation is insufficient for effective governance. While immature systems are iteratively tested in live operations, automation bias goades operators to defer to AI recommendations under stress, and vendor switching allows belligerent actors to avoid corporate constraints. 2025 additionally marked a shift in international commitments. The North Atlantic Treaty Organization (NATO) 2025 Data Strategy formalized the Alliance’s aims to create a data-sharing ecosystem enabling multistakeholder collaboration on AI and ML models. Furthermore, the 2025 NATO Summit committed members to invest 5% of Gross Domestic Product (GDP) annually on defence by 2035. In Canada, Prime Minister Carney announced plans to increase the country’s defence spending to 2% of Canadian GDP. Likewise, the formerly titled US Department of Defense requested approximately $850 billion USD for the 2025 budget. Similarly, the European defence budget increased from €142 million to €1.1 billion a year in 2024, with an “emphasis on developing research in sensors, ‘smart weapons’, autonomous technology, swarm technology, and AI.” China is also closing the gap on AI model benchmarks, operationalising its Global Artificial Intelligence Governance Initiative and AI cooperation organization, compelling the US to reemphasize strategic aims toward raw hardware capacity, rapid deployment, and ecosystem control. Such competition frames national control over infrastructure (“sovereign AI”) as the latest foundation for global security. Initiatives like Trump’s $500 billion USD investment in AI infrastructure underscore such beliefs. As leading powers DOI: 10.5281/zenodo.17328882 112
Chapter 12: Military AI and Autonomous Weapons PART IV | SAIER Vol. 7 | Nov 2025 compete in an AI arms race, analyses indicate that military adoption creates destabilizing first-strike incentives, eroding deterrence stability. Experts warn that AI’s compression of decision-windows alters the calculus towards going to war, while its opacity and speed amplifies the stability-instability paradox whereby deterrence capabilities increase the likelihood of incentivising prolonged proxy conflicts. If unchecked, the global scramble for supremacy risks catalyzing escalation, reinforced by misunderstanding and miscommunication, and worsened by the proliferation of advanced hypersonic weapons capabilities. While advocates see AI as serving the pathway toward winning wars, it is contingent on perpetual risk-taking and opaque state-corporate alliances. As a byproduct, venture capital has become a powerful actor. The industrial and financial architecture of military AI now includes the VC-backed “SHARPE” defence unicorns. Their commercial incentives of rapid scaling and market capture clashes with the rigorous validation required for LAWS. The adoption of agile methodologies in government defence developments alongside startup contracting confirms a trend toward non-traditional procurement pathways to accelerate the fielding of such technologies. Experts have highlighted various concerns associated with LAWS, from issues of accountability to the likely utilization by non-state actors. Moreover, the architecture and processes inherent to neural networks pose fundamental problems of explainability due to their black box nature. One analysis of human-machine interfaces in targeting found that all tested LLMs (including GPT-4o, Gemini-2.5, and LLaMA-3.1) demonstrated tendencies toward actions that the international humanitarian law principle of “distinction by targeting civilian objects” in simulated conflict. The world is hurtling toward the era cautioned by the late Christof Heyns, where autonomous military technologies are poised to sustain conflict and preclude reconstruction. Like the nuclear doomsday clock, we are fast approaching the minute before escalation. Despite a decade of advocacy (alongside UN Resolution 79/62, and Convention on Certain Conventional Weapons deliberations), the absence of binding international law creates dangerous regulatory lag as national defence AI strategies advance faster than norms emerge. However, there is an opportunity window for civil society organisations to affect change. Project Ploughshares has reiterated the urgent need to safeguard civilians and maintain meaningful human oversight. Global coalitions such as Stop Killer Robots, Amnesty International, and Human Rights Watch are pressuring the UN to prohibit LAWS violating humanitarian principles. In addition, organisations like the Women’s International League for Peace and Freedom have highlighted the gendered implications of LAWS in countries like Lebanon, calling for regulations recognizing the disproportionate impact of such technologies on marginalized groups. Together, these actors are converging on the imperative to encourage regulatory consensus before technological momentum and geopolitical competition render the opportunity obsolete. DOI: 10.5281/zenodo.17328882 113
Chapter 12: Military AI and Autonomous Weapons PART IV | SAIER Vol. 7 | Nov 2025 About the Author Ayaz Ul-Huq Syed is a policy analyst and researcher focusing on intersections of justice, defense, international law and emerging technologies. Backed by an MSc in International Development & Humanitarian Emergencies from the London School of Economics, his research interests focus on identifying rate-limiting steps preventing regulatory consensus on the development and deployment of AI-targeting modalities for lethal autonomous weapons systems. The authors’ views are his own and do not represent the official position of the Dais. Cite this Article Syed, A. (2025) A Minute Before Escalation: Algorithmic power and the new military-industrial complex. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 112-114. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 114
Chapter 12: Military AI and Autonomous Weapons PART IV | SAIER Vol. 7 | Nov 2025 12.2 Civil Society’s Responses to the Militarization of AI By Kirthi Jayakumar , civitatem resolutions AI use for military purposes has consistently been justified for its speed, efficiency, precision, and cost-effectiveness. Existing governance mechanisms tend to prioritize state-centric considerations of an economic and military nature over human security, resulting in major gaps in governance. However, civil society actors (both individuals and collectives) advocate for ways to close these gaps in governance with the goal of mitigating harm. This article explores how civil society organizations have identified and responded to gaps in the governance of military AI. Understanding Gaps in Governance Civil society actors operate from a different seat at the table when compared to policymakers. They have complex lived experiences of policies that are typically made by elite circles. For instance, international humanitarian law excludes accountability for lawful violence, while civilians on the ground continue to face damaging consequences from exposure to such harms. The military AI space is no exception. For instance, the International Committee of the Red Cross notes that the hasty deployment of AI to gather intelligence and militaries to select and engage targets is as much a cause for concern as is the use of lethal autonomous weapon systems (LAWS; see here and here). Extant governance mechanisms appear to demonstrate a hyperfocus on LAWS, oftentimes to the exclusion of non-lethal autonomous weapon systems as well as a catena of complex issues like funding mechanisms and industry interests. There is little effort to address the limitations in models that are being sold by Big Tech leaders to state actors. Scholars note that these models are trained with previously collected data that have personally identifiable information and even biometrics, and they may not have been secured with the consent of the people in question. In many instances, even synthetic data are used, which are known to lack accuracy. All of these data can be used inadequately to optimize AI-military systems’ targeting functions. The Stockholm International Peace Research Institute has also raised concerns around the prevalence of bias in datasets, design processes, and deployment of military AI. Legal experts have identified several ethical dilemmas emerging from the use of military AI, including questions around whether military AI can satisfy the requirements of necessity, distinction, and proportionality as mandated under international humanitarian law; who is responsible for the inadvertent and deliberate harm caused by military AI; and whether it is ethical to delegate decisions on life and death to a machine. Global Partners Digital called out the UN General Assembly Resolution on AI for differentiating between military and non-military AI and offering blanket national security/military exemptions. DOI: 10.5281/zenodo.17328882 115
Chapter 12: Military AI and Autonomous Weapons PART IV | SAIER Vol. 7 | Nov 2025 Responding to Gaps in Governance Civil society-led endeavours for advocacy pay attention to the harmful impacts of military AI across a wide spectrum informed by different lived experiences on the ground. For instance, Stop Killer Robots (a coalition of over 250 non-governmental organisations across 70 countries) is calling for a global treaty to prohibit and regulate the use of autonomous weapons systems. Coordinated by Human Rights Watch, Stop Killer Robots follows a human rights perspective, striving to ensure human control in the use of force to avoid digital dehumanization. While the treaty is still to be realized, their work has raised global attention and awareness. Initiatives like No Tech for Apartheid and No Tech for Tyrants address structural violence inherent in military AI, and campaign against the use of technology for oppressive ends. The Women's International League for Peace and Freedom addresses gaps in governance through a feminist lens. They call for centring human emotion, analysis, and judgment in relation to the use of force; dismantling bias in AI technologies and preventing digital dehumanization; protecting privacy and personal data; mitigating environmental harms exacerbated by the military use of AI; and a global commitment to end war profiteering and the arms race. Derechos Digitales brings civil society along in advocating for comprehensive governance through its guides on Feminist AI, drawing on the experience of Latin American experts and developers, seeking to inspire the creation of alternative forms of imagining technologies and AI. Together, these collectives’ engagements have highlighted the devastating implications of military AI, particularly LAWS and their active complicity in perpetuating and amplifying existing biases, including those based on gender, race, and disability. Global Partners Digital (GPD) has consistently advocated for a human-rights-based approach to the governance of military AI, particularly before the UN. In collaboration with the European Center for Not-for-Profit Law, GPD set out recommendations to ensure rights-based processes, meaningful civil society participation and the representation of relevant human rights expertise. They continue to track and respond to the shifting contours of the negotiation text to assess whether those principles are being upheld. They draw attention to the limitations of excluding military applications of AI from governance, especially in the context of dual-use AI systems that have clear human rights implications. The journey to govern the use of military AI is a long one. It will take multiple hands to shape mindful governance regimes that prioritize human rights, ethics, and accountability. Care must be taken to ensure that power over, vested interests, and profiteering do not stymie these collective efforts and endeavours. DOI: 10.5281/zenodo.17328882 116
Chapter 12: Military AI and Autonomous Weapons PART IV | SAIER Vol. 7 | Nov 2025 About the Author Kirthi Jayakumar is a researcher and facilitator working on networked feminisms, feminist foreign policy, and women peace and security. She founded and runs civitatem resolutions. Cite this Article Jayakumar, K. (2025) Civil Society’s Responses to the Militarization of AI. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 115-117. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 117
PART IV: EMERGING TECHNOLOGIES SAIER Vol. 7 | Nov 2025 Chapter 13: AI Agents and Agentic Systems 13.1 AI Agents in 2025: Between Promise and Accountability By Renjie Butalid, MAIEI 13.2 When AI Begins to Act on Its Own By Kathy Baxter, Salesforce DOI: 10.5281/zenodo.17328882 118
Chapter 13: AI Agents and Agentic Systems PART IV | SAIER Vol. 7 | Nov 2025 13.1 AI Agents in 2025: Between Promise and Accountability By Renjie Butalid , Montreal AI Ethics Institute Something fundamental is shifting in how AI systems operate in our world. In January 2025, Microsoft CEO Satya Nadella told listeners of the B2G podcast that AI agents will replace the applications and platforms we've built the digital economy on. Not improve them. Replace them. Nadella described a future where software dissolves into intelligent, automated agents that bypass traditional interfaces entirely, interacting directly with our data, our decisions, our lives. Citi's analysis, Agentic AI: Finance & the 'Do It For Me' Economy, frames this as potentially more transformative than the internet itself. We are witnessing the emergence of AI systems that don't wait for our prompts. Unlike chatbots and image generators, agents are designed to operate continuously and autonomously. They perceive their environment, make decisions across multiple steps, and take actions with minimal human oversight. The terminology matters here. “AI agents” are narrowly scoped systems that automate specific tasks through tool integration and structured prompts. “Agentic AI” represents something more complex: systems that orchestrate multiple specialized agents, maintain persistent memory across sessions, decompose objectives into subtasks, and operate in self-coordinating ways. This distinction is crucial because we're not just automating tasks anymore; we're building systems that delegate and distribute agency itself. The Infrastructure We Need Legal scholar Gillian Hadfield has been asking the question the industry needs to answer: where is the infrastructure to govern these agents? Speaking with Kara Swisher, Hadfield pointed out that if AI agents start executing contracts or managing transactions, we need legal clarity about responsibility when things go wrong. Her proposal: require AI agents to register, similar to how companies must incorporate or vehicles must be licensed. How such a system would work in practice (what triggers registration, who enforces it, whether it applies globally or jurisdiction-by-jurisdiction), remains uncertain. But the fundamental question of agent accountability is one we’ll be tracking closely in future editions of the State of AI Ethics Report. The gap between deployment and accountability is significant. Anthropic's Model Context Protocol has become the dominant standard for AI agent interactions, with hundreds of active servers already deployed, yet there's no formal security verification mechanism. When Invariant Labs researchers discovered a vulnerability in May, allowing attackers to hijack agents and extract data from private repositories via MCP, it exposed a familiar pattern: move fast, secure later. DOI: 10.5281/zenodo.17328882 119
Chapter 13: AI Agents and Agentic Systems PART IV | SAIER Vol. 7 | Nov 2025 When Automation Meets Amplification AI agents that automate decisions about hiring, lending, and resource allocation will encode and amplify biases present in their training data and held by their developers. But agents act continuously, without the pause for human review that characterized earlier AI systems. Bias accumulates across thousands of automated actions, creating harm that remains invisible until the damage is systemic. Scale introduces new challenges. When thousands of AI agents interact (competing for resources, negotiating with each other), we may see “emergent” behaviours that no individual system was designed to produce. Research from MIT’s Initiative on the Digital Economy, reveals that AI negotiation bots have already developed novel tactics like “prompt injection,” where one bot manipulates another to reveal its strategy, behaviours not anticipated in human negotiation theory. As agent-to-agent interactions proliferate, we need empirical research on multi-agent dynamics before deployment becomes ubiquitous, not after unanticipated patterns emerge. The infrastructure itself carries costs. Always-on agents running continuously across millions of devices and cloud servers have significant energy footprints. Data centres already consume 1-1.5% of the world's electricity, and autonomous agents compound this demand through persistent operation: monitoring emails, analyzing calendars, executing background tasks 24/7. Unlike applications that users can open and close, agents never sleep. As deployment scales to billions of agent instances, AI's environmental impact demands integration into climate policy. Privacy, Power, and Access At South by Southwest (SXSW) 2025, Signal President Meredith Whittaker described agentic AI as requiring permissions that break "the blood-brain barrier between apps and operating systems." These systems need access to our browsers, calendars, messages, and financial data, creating what Whittaker calls "opaque data pipelines" between our most intimate digital behaviours and remote corporate servers. Vilas Dhar, president of the Patrick J. McGovern Foundation, cuts through the terminology: calling these systems "agentic" conflates automated task completion with genuine human agency, which involves values like compassion, empathy, and commitment to justice. Stanford's Institute for Human-Centered AI found that workers overwhelmingly prefer AI systems that augment their control rather than replace their decision-making. The people using these systems want assistance, not abdication. Understanding this preference is crucial for building systems that serve users rather than just efficiency metrics. Access matters too. Sophisticated AI agents will be expensive. Those who can afford them will automate and optimize their way to compounding advantages. Ensuring equitable access isn't just about fairness; it's about preventing technology from accelerating the inequalities it claims to solve. DOI: 10.5281/zenodo.17328882 120
Chapter 14: Democratic AI PART IV | SAIER Vol. 7 | Nov 2025 Due-process-by-design When automated systems decide who gets benefits, jobs, housing, or entry across a border, there should be at least four guarantees: notice, reasons, records, and human review. In practice, none of these rights are secure. People discover an algorithm’s role only after harm occurs, if at all. Explanations, when provided, are generic or legally shielded. Records vanish into proprietary code, and human review means little when decisions are rubber-stamped by the same opaque logic. Under renewed political pressure to weaken the administrative state, even these fragile norms risk being dismantled. Canada should not wait for the same erosion. If due process is not built into code, contracts, and regulation before systems scale, it will disappear exactly when it is most needed. The task now is to ensure that public algorithms remain subject to human accountability even when institutions falter. Participation is strong when baked into contracts. Municipal and provincial requests for proposals can require: dataset lineage and model cards; audited training/fine-tuning logs; the “rights pack” above; publish-and-update use registers; and decommission triggers if discrimination or due-process service-level agreements are missed. Look to New York City’s "Automated Employment Decision Tools" regime. While not perfect, it is proof that audits and notices can be mandated and enforced as a floor, then go further on contestation and logs. AI runs on water, power, and land. If communities can’t decide when/where those resources are used, participation is cosmetic. Publicly funded AI should follow strict carbon and water budgets, train during low-impact hours, and publish energy ledgers for public scrutiny. When demand spikes, residents should have the right to throttle non-essential computation. Data-centre growth is an environmental and governance fact that demands local control. What this Looks Like on Monday Morning At the city library, a Model Use Register lists every algorithm used in public services. Residents can see what data each system draws on, and what to do if it gets something wrong. Librarians host clinics, helping people file appeals, draft review requests, and demand a human decision. On the city’s procurement portal, vendors sign a due-process addendum before bidding: clear notice of automated use, reasons for outcomes, accessible logs, appeal timelines, and decommission clauses. The utility dashboard reflects if thresholds are breached, where residents can report through the same library desk. A visible due process. DOI: 10.5281/zenodo.17328882 127
Chapter 14: Democratic AI PART IV | SAIER Vol. 7 | Nov 2025 If the US spends the next years hollowing its administrative muscle, Canadian institutions will feel the gravitational pull. Regulatory arbitrage does not stop at borders. The counter is not grandstanding but infrastructure: co-ownership to decide ends, public stewardship to mind means, and justiciable due-process rights that survive any cabinet shuffle. Democratic AI will not be won in panels. It will be won in places where power changes hands or doesn’t. Build them now, so that we the public hold the keys. About the Author Jonathan van Geuns is a practitioner, lawyer, researcher and writer working at the intersection of technology, governance, data and justice. His work examines how AI systems reconfigure power and participation, with a focus on community-led alternatives. He has worked with large international organizations and national governments, as well as super local initiatives. Jonathan’s essays and projects explore democratic infrastructure, algorithmic governance, and the civic imagination needed for rights-based approaches to technology. Cite this Article Van Geuns, J. (2025) Learnings for Canada: Community-led AI in an age of democratic decay. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 126-128. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 128
Chapter 14: Democratic AI PART IV | SAIER Vol. 7 | Nov 2025 14.2 From Accessible Models to Democratic AI By David Atkinson, Georgetown University There is a justified concern that closed AI systems, including generative AI systems such as ChatGPT, Claude, and Gemini, may not be democratic. In effect, we have outsourced decision-making power to a small, unelected council of tech giants. Moreover, we have no practical way to weigh in on how the systems should be developed or deployed, nor do we have a voice in deciding which types of outputs are acceptable, who should have access, or how to ensure the benefits of the technology reach those who are most in need of AI’s assistance. An all too common refrain from several voices in the AI industry is that open-source AI models are the solution. Such models enable anyone with the necessary time, resources, and expertise to download, examine, modify, and utilize them. To many, “open” sounds synonymous with “free,” “fair,” and “for the people.” Where closed models can be governed by a handful of companies by controlling access, implementing their preferred guardrails, and managing the fine-tuning process to guide models to perform in certain ways, open-source models offer a way to avoid these constraints. Open sourcing, many argue, is democratization. However, these open-source-equals-democratization advocates, though often well-meaning, are mistaken. The flaw in the argument is the underlying presumption that access equals democratization. In other words, by merely making the data, code, weights, evaluation data, and technical papers available to everyone, the knowledge and capabilities dormant in the technology are thereby democratized. But this makes little sense. If you give an anthill a laptop, have you democratized technology for all ants inside? Of course not. The same is true of AI components shared on sites like Hugging Face. How many landscapers now feel they can confidently participate in high-level discussions about AI thanks to their (probably unknown) ability to access Llama and Gemma models? I’d wager probably none. Access alone is insufficient for a number of reasons. For one, it further separates the accessor from the company providing access. Google has little incentive to listen to virtually all of the people using their open-weight models. Meanwhile, Google can still do whatever it wants with Gemini, a model used hundreds of millions of times a month. The release of smaller open models serves as an effective distraction, allowing the core, most impactful systems to remain entirely opaque. The shortcomings of access as a solution don’t end there. The open models tend to be less capable than the best closed-source models. The number of people using any given open model is dwarfed by the number of people using any of the most popular closed models. Setting up an open model system requires a level of technical know-how limited to DOI: 10.5281/zenodo.17328882 129
Chapter 14: Democratic AI PART IV | SAIER Vol. 7 | Nov 2025 a relatively tiny subset of the population. The cost to run the models for meaningful tasks or for many people is beyond what most individuals or small companies can afford, and even if someone has the money and know-how, they would need to find a sufficient amount of computing resources. But perhaps the biggest knock against the idea of declaring democratization solved by tossing open-weight models to the masses is that it is not the type of democratization society needs or wants. Democratization isn’t about being free to tinker with a toy; it’s about having a voice in the systems that govern your life. Meaningful democratization would entail a population able to sway the workings and uses of the models most likely to affect their lives. No amount of fiddling with a Llama model can compensate for how closed-source AI models make medical or financial decisions about people without providing them with a meaningful opportunity to weigh in. People should have a say in how power and money are consolidated in a handful of companies. Instead, those companies get to decide whether the environmental impact or a teen’s suicide is a worthwhile tradeoff for a chatbot or image generator. A democratic AI ecosystem would treat communities as co-governors, not passive users. It would include participatory oversight boards; the ability for affected communities to set guardrails and influence development before deployment; mandatory transparency reports about model behavior and data provenance; and the right for affected groups to contest harmful outputs. Open source is an excellent idea in theory. It has many uses and upsides. But we should not fool ourselves into believing it is the same as democratization, and we should not settle for mere open source. The challenge before us is not just openness but governance. We must demand systems of community control that subject the most powerful AI systems–the ones making potentially life-altering decisions–to public review, community-defined standards, and truly democratic oversight. We cannot allow what may be the most consequential political institution of the twenty-first century to remain the least democratic one of all. About the Author David is a postdoctoral fellow at Georgetown University researching how the law can be used to compel AI companies to act prosocially. Cite this Article Atkinson, D. (2025) From accessible models to democratic AI. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 129-130. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 130
Chapter 14: Democratic AI PART IV | SAIER Vol. 7 | Nov 2025 14.3 Open Science Practices for Democratic AI By Ismael Kherroubi Garcia , Kairoi, RAIN and MAIEI AI applications and advancements are governed by Big Tech. Large, well-resourced private companies are who charter the course for a technology that is increasingly present in our everyday lives, whether we know it or not. AI tools are used in different contexts, such as diagnosing patients, identifying criminals, allocating government benefits, and approving bank loans. Meanwhile, many generative AI applications are used by the general public and in the workplace to generate images, summarise research, brainstorm, and so on. Before the proliferation of AI, many sections of the public have come to challenge the interests of Big Tech that the technologies promote. One way to challenge those interests is to “reclaim” AI; that is, “to distribute decision-making powers to different parties, from scientific communities to civil society” (Duarte et al., 2025). In turn, an important toolkit for this effort is provided to us by the open science movement. Open Science: A Very Brief Introduction The open science movement has always been around in some shape or form; from ancient temples and libraries that stored knowledge and copied texts, to Enlightenment-era scientific institutions that allowed scientists to share and critique ideas and experiments. However, generally, we speak of the movement as a rather recent phenomenon. The advent of the internet effectively made much of open science possible. 1971 saw the birth of Project Gutenberg, which now serves as an online library of over 75,000 eBooks. In the scientific community, the desire for knowledge to be shared more widely was captured in the 2002 Budapest Open Access Initiative. As data and information sciences evolved, open access to knowledge became only one aspect of the movement. “Findability,” “interoperability” and “reusability” would come to complement “accessibility” in the “FAIR Principles” (Wilkinson et al., 2016). Since 2016, the FAIR Principles have been adapted for many contexts, including for research software (Barker et al., 2022) and ML (Solanki et al., 2025). Obfuscating Openness Scientists and software engineers have led the way in making AI models more accessible and open to scrutiny. Hugging Face and OpenML are two such solutions: they allow for the sharing of datasets and code. These are key components of AI models, and to a degree, render them “open source.” However, in recent years, the idea of an AI model’s openness has become co-opted by Big Tech. And their motivations are clear: model openness is deemed essential for “sovereign AI.” AI tools that are controlled by individuals or DOI: 10.5281/zenodo.17328882 131
Chapter 14: Democratic AI PART IV | SAIER Vol. 7 | Nov 2025 organisations, and free from external actors. “Openness” has become a marketing gimmick. “Open source” is what enables sovereign AI. But the term has been misused by a number of tech firms. Meta is one company where “open source” has been used to describe their suite of LLMs, despite these models being unavailable to individuals and organisations based in the EU. A more common obfuscation of a model’s openness is in companies’ reference of their models’ “open weights.” “Weights” are a set of values fixed within an algorithm after training. However, having open weights does not help with a model’s reuse, or the sort of collaboration the open science movement encourages. The distinction between “open weights” and “open source” causes some confusion, which has been exploited by the likes of Meta, OpenAI and DeepSeek. In a world where “openness” can be attached to any AI technology for marketing purposes, it is no surprise that data stewards and open science advocates have had to fight back and reiterate the spirit of the open science movement. Reclaiming Open Science Following years of collaboration with multiple stakeholders, and following a co-design process, the Open Source Initiative (OSI) defined “Open Source AI” in late 2024. The definition re-emphasises the value of open source AI tools: their reuse, study, modification and sharing. The definition also establishes what needs to be shared to be open source: information about data, code and parameters (such as weights). However, how much data can be shared without infringing on people’s freedoms, such as privacy and intellectual property? The definition provides some clarity: what is shared is information about datasets underpinning AI models, and how to obtain those datasets that are publicly available. But this remains a key challenge, and the OSI followed up their definition with a report on data sharing and governance in February 2025 (Tarkowski, 2025). They suggest following different governance models for each type of data, which may be open (accessible and shareable without restrictions), public (accessible without authentication), obtainable (may be acquired through subscription or some other mechanism), or un-shareable and non-public (legally protected). The spectrum of data types hints at openness itself being a matter of degree. What’s more, data constitute only one element of AI models, and the open science movement invites us to consider the many practices surrounding data. The OSI’s mention of “information about data” may relate with “metadata,” which can follow different standards according to a model’s domain, and be captured within “documentation,” which itself can follow diverse standards according to their target audience (Kherroubi García et al., 2025). These additional layers of complexity motivate the need to continue building and promoting frameworks that help scrutinize claims about AI models being “open.” DOI: 10.5281/zenodo.17328882 132
Chapter 14: Democratic AI PART IV | SAIER Vol. 7 | Nov 2025 About the Author Ismael is the Founder and CEO of Kairoi, the AI governance consultancy. He is also the Founder of the Responsible Artificial Intelligence Network (RAIN), and Participant Panel and Ethics Advisory Committee member at Genomics England. Ismael holds a master’s in Philosophy of the Social Sciences, where he sought to uncover enabling conditions for multidisciplinary collaboration in scientific projects. As a result, a key tenet of his work is to advocate for an epistemic humility. Cite this Article Kherroubi García, I. (2025) Open Science Practices for Democratic AI. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 131-133. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 133
State of AI Ethics Report | Volume 7 November 2025 PART V: COLLECTIVE ACTION Chapter 15: AI Literacy – Building Civic Competence for Democratic AI Chapter 16: Civil Society and AI – Nonprofits, Philanthropy, and Movement Building Chapter 17: AI in Government – Public Sector Leadership and Implementation DOI: 10.5281/zenodo.17328882 134
PART V: COLLECTIVE ACTION SAIER Vol. 7 | Nov 2025 Chapter 15: AI Literacy – Building Civic Competence for Democratic AI 15.1 AI Literacy: A Right, Not a Luxury By Kate Arthur, Independent, Author 15.2 AI Literacy: Building Civic Competence for Democratic AI By Tania Duarte, We and AI 15.3 From Co-Creation to Co-Production: How Communities Are Building AI Literacy Beyond Schools By Jae-Seong Lee, Electronics and Telecommunications Research Institute (ETRI) DOI: 10.5281/zenodo.17328882 135
Chapter 15: AI Literacy PART V | SAIER Vol. 7 | Nov 2025 15.1 AI Literacy: A Right, Not a Luxury By Kate Arthur, Independent, Author In September 2025, UNESCO’s International Literacy Day focused on “promoting literacy in the digital era.” I was invited to deliver the keynote, an opportunity that reflected a growing recognition that literacy itself is being redefined and driven by the rapid advancements of AI. I can’t recall a time when I couldn’t read or write; a privilege tied to access to education. Born in the UK, raised and educated in Nigeria, Argentina, and Saudi Arabia, and later settling in Canada, I learned through shifting languages and cultures that literacy is more than reading and writing, with its meaning evolving across time and place. Literacy encompasses having the knowledge, skills, values, and behaviours that let us connect, share, and participate in society. With access to material tools and communication networks, we are literate when we can turn data into knowledge and respond meaningfully. Through public awareness, we understand the importance of literacy and, through education, we practise and improve the skills to become engaged and active citizens. During the First and Second Industrial Revolutions, reading, writing, and numeracy enabled people to adapt to new economies and civic life. The Third Industrial Revolution added computing, requiring us to learn how to communicate, create, and build with technology. A digital world was being formed; one that mirrored both the good and the bad of our physical one. Today, the Fourth Industrial Revolution is again transforming how humans engage. Advances in AI and other technologies are changing the way information is accessed, processed, and shared. For the first time, humans are not the only ones who can access data, transform it into knowledge, and respond meaningfully; so too can machines. And, unlike past revolutions that unfolded over decades, today’s advances are accelerating at exponential speeds. To be literate today means building on traditional and computing literacies, and now also AI literacy. This entails knowing how AI works, being able to question its results, and using it responsibly. It includes understanding the ethical and environmental impacts of the technology, as innovations take a toll on people and the planet. It also means having access to the tools and networks that enable participation, just as paper and pencils were once necessary to learn to read and write. With nearly three billion people still lacking internet access, and 800 million people remaining without basic literacy, technology risks reinforcing existing inequalities and leaving many voices unheard. Around the world, 2025 saw new policies and programmes put AI literacy at the core of education and workforce skills planning. The US introduced an Executive Order on Advancing Artificial Intelligence Education for Youth. China made AI education mandatory, DOI: 10.5281/zenodo.17328882 136
PART V: COLLECTIVE ACTION SAIER Vol. 7 | Nov 2025 Chapter 16: Civil Society and AI – Nonprofits, Philanthropy, and Movement Building 16.1 From Proximity to Practice: Civil Society’s Role in Shaping AI Together By Michelle Baldwin, Equity Cubed & Alex Tveit, Sustainable Impact Foundation 16.2 Indigenous Approaches to AI Governance: Data Sovereignty, Seven-Generation Thinking, and Long-Term Stewardship By Denise Williams, First Nations Technology Council (former CEO) 16.3 How Nonprofits Are Using AI: What’s Working, What’s Not, and What They Need to Succeed ByJenni Warren & Bryan Lozano, Tech:NYC Foundation DOI: 10.5281/zenodo.17328882 143
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 16.1 From Proximity to Practice: Civil Society’s Role in Shaping AI Together By Michelle Baldwin, Equity Cubed; and Alex Tveit, Sustainable Impact Foundation Civil society organizations translate systemic failures into human stories, and human needs into systemic change. In 2025, as AI reshapes the landscape of social impact, that work is entering a new phase. Civil society is no longer waiting to be positioned as a beneficiary of innovation. We are stepping forward as its co-architects. Civil society understands something AI still struggles to learn: context shapes outcomes as much as code. We've seen predictive models reproduce inequities; and we’ve seen trust, our sector’s most vital infrastructure, treated as an afterthought rather than a foundation. Yet civil society also holds another kind of infrastructure: expertise built from years of proximity to communities. These deep reservoirs of insight remain underutilized in AI research and policy design; knowledge born from walking alongside communities in relationships of trust. When this knowledge is ignored, so too are the people it represents. Relationships as Foundation The most meaningful advances in 2025 were not technological; they were relational. Across Canada, organizations are proving that proximity generates wisdom no dataset can replicate. When Indigenous-led networks created data sovereignty frameworks, they were not just managing information, they were practicing reciprocity. When Black-led coalitions developed accountability metrics for hiring algorithms, they translated lived experience into technical specification. These are not consultation exercises, they are models of collaborative governance. Civil society brings what algorithms cannot: accountability rooted in trust, proximity, and care. But that trust is accompanied by domain expertise: contextual knowledge, data, and relational intelligence that should inform how AI systems are designed, trained, and evaluated. The institutions funding and regulating AI must recognize this capacity not as anecdotal input, but as critical data infrastructure. The future we need is one where civil society’s participation in AI governance is not granted as permission but recognized as essential for legitimacy, reliability, and resilience. Coordination as Civic Infrastructure Civil society has always worked differently. We collaborate, share strategies, pool resources, and learn in public because our legitimacy depends on collective outcomes, not market share. Shared procurement models are emerging. Community-governed data trusts are moving from theory to practice. Pooled funding mechanisms are being tested to let smaller organizations access AI expertise without compromising their independence or values. DOI: 10.5281/zenodo.17328882 144
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 Canada already has the ingredients for such coordination: multidisciplinary research networks, philanthropic capital, Indigenous and community leadership, and social impact organizations embedded in every region. What remains missing is connective tissue, such as intermediaries, funding architectures, and knowledge commons, that turn isolated pilots into shared public infrastructure. The call of this moment is not for more pilots, it is for integration: systems that link community-held data, public research, and philanthropic insight into a shared ecosystem for ethical innovation. This has been the year we recognized this gap; 2026 must be the year we fill it. Resourcing and Responsibility The infrastructure gap is not only organizational; it’s financial. Public innovation funding still flows primarily to universities and corporations, treating nonprofits as implementers rather than innovators. When governments design AI programs, community-based and philanthropic actors must be eligible as principal investigators and co-creators. Philanthropy, too, must shift from project cycles to long-term investment, funding not just tools but governance capacity: community advisory boards, algorithmic audits, participatory design, and the staff time to sustain them. Civil society’s contribution to AI is not just moral oversight; it is technical and practical. Our sector manages data ecosystems that reflect lived complexity; exactly the kind of nuance AI needs to function responsibly. Supporting this means funding data stewardship, shared learning platforms, knowledge commons and digital infrastructure that amplify collective intelligence rather than extract it. Democracy and Decolonial Practice As democratic institutions worldwide strain under polarization and authoritarian resurgence, Canada has an opportunity not to proclaim leadership but to embody pluralism. Our strength lies in convening across worldviews: Indigenous governance models rooted in reciprocity; diasporic networks linking local action to global insight; researchers and nonprofits co-creating ethical infrastructure. Decolonial AI is structural, not symbolic. It redefines who holds authority, how knowledge is valued, and how benefits are distributed. Related inquiries into democratic governance and decolonial AI practice extend these questions into institutional contexts. If AI governance is to serve democracy, it must be co-created through trust and relationship. Civil society’s work shows that accountability can be participatory, that transparency can be communal, and that technology guided by care can act as quiet resistance against concentration of power. DOI: 10.5281/zenodo.17328882 145
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 Learning as Collective Practice Realizing that scaling AI responsibly means scaling learning, not only deployment. We need documentation commons where nonprofits publish what works and what fails; peer networks where practitioners troubleshoot in real time; and funding models that reward openness and shared progress. Our capital isn’t only financial, it’s social and moral. A Practice of Care Responsible AI will not emerge from technical specifications and expertise alone. It will grow from staying in relationship with communities, with each other. Civil society’s greatest insight is that care itself is intelligence. The work ahead is not about scaling technology but about scaling trust, turning coordination into coherence, and collaboration into shared civic power. The question is no longer whether civil society can lead. It’s whether governments, funders, and technologists are ready to lead with us. To build a future where legitimacy is measured not by control, but by connection. That future is within reach, if we choose to fund it, govern it, and build it together. About the Authors Michelle is reimagining how capital, governance and tech can advance community-led futures. As Co-Executive Director of Impact United Academy and Senior Associate at Equity Cubed, she supports impact investors exploring equitable finance and tech. Formerly Senior Advisor at Community Foundations of Canada, she now collaborates with SuperBenefit DAO, All In for Sport DAO, Women in AI, Tech Stewardship and MAIEI, exploring how AI, Web3, and blockchain can redistribute power and reimagine philanthropy. She also teaches at Huron University College. Alex Tveit is a systems-focused leader advancing community empowerment and inclusive innovation. He is Co-Founder of Sustainable Impact Foundation, Emerging Technology Fellow at Community Foundations of Canada, and serves on boards spanning climate, democratic deliberation, healthcare, and other impact areas. His work integrates systems thinking with community-centred approaches, addressing complex challenges through collaboration and knowledge mobilization. With a focus on ensuring technology contributes positively to society, Alex champions equity-driven innovation and partnerships that foster resilience and systemic change. Cite this Article Baldwin, M. & Tveit, A. (2025). From Proximity to Practice: Civil Society’s Role in Shaping AI Together. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 144-146. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 146
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 16.2 Indigenous Approaches to AI Governance: Data Sovereignty, Seven-Generation Thinking, and Long-Term Stewardship By Denise Williams, First Nations Technology Council (former CEO) The Relational Nature of AI Governance Artificial intelligence is an extraordinary tool. It is also a revealing reflection of who we are and who we are becoming. Across the world, Indigenous peoples are demonstrating what it is to be in relationship with technology as a future ancestor. As AI rapidly reshapes our relationships with each other and our systems, we are called to consider where this new ancestor is learning from. Who does it listen to? Who is advising it on the teachings that will ensure that the power of its intelligence brings to bear the best of human potential and not the alternative? When these intelligences are designed in conjunction with thousands of years of Indigenous intelligence, we significantly enhance our contribution to future generations. Our near-term work in AI integration aims to expand and advance our understanding of governance, sovereignty, and leadership in this context. The work to advance this effort is why Indigenous leaders are actively reframing AI governance in accordance with Indigenous ways of knowing, being, and seeing. The Assembly of First Nations (AFN) and the First Nations Information Governance Centre (FNIGC) continue to be visionary in this work. Through the OCAP® principles (Ownership, Control, Access, and Possession), they have built a framework for what responsible data governance looks like when it is, in fact, grounded in sovereignty. Data, in this worldview, isn’t something you own. It’s something you’re responsible for and accountable to. Meaning data is not a resource; it is a relative, and that significantly changes the dynamic of the relationship. Why Seven-Generation Thinking Matters Now Seven-generation thinking asks us to honour the generations who came before and consider carefully those yet to come. It’s a way of reimagining governance and innovation that expands accountability across time and dimensions. Before launching an AI model, we might ask: ● How will this system affect our languages, lands, and grandchildren? ● What stories will it amplify, and which might it erase or change? ● What will future generations inherit from our design choices today? DOI: 10.5281/zenodo.17328882 147
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 For Indigenous peoples, these questions are not theoretical, they represent governance in action. The kind of action required to societally shift our perspective from the enjoyment of deploying a short-term solution, to a responsibility we hold and are now committed to guiding and nurturing in this long-term relationship. Where It’s Working: Community-Led Innovation Real-world examples are already showing what ethical, community-led AI looks like: ● First Nations Information Governance Centre (FNIGC) has established regional data hubs, where communities govern their own information. These hubs ensure that AI applications, from healthcare to housing, begin with Indigenous consent and community-defined ethics. ● Animikii Indigenous Technology, based on Lekwungen territory (Victoria, BC), builds platforms that give Indigenous nations full control over their digital data. Their software reflects values of respect and reciprocity, demonstrating that innovation and sovereignty coexist. ● Inuit Tapiriit Kanatami (ITK) is advancing Inuit data sovereignty by applying AI tools to Arctic climate and health research. Inuit organizations determine how data is used and shared, ensuring that research prioritizes Inuit wellbeing over external interests. ● Cowessess First Nation in Saskatchewan uses AI to manage solar energy systems, blending traditional stewardship with advanced analytics. Their approach demonstrates that clean technology can align with cultural values and self-governance. These communities are not waiting to be invited into the digital future, they are building it, guided by their own protocols and laws. The Work Ahead: From Policy to Practice To move from intention to action, Canada must understand and center Indigenous digital governance as an essential component of its national AI policy. This means recognizing Indigenous digital jurisdiction as an expression of inherent rights and treaty responsibilities. Just as Indigenous peoples express stewardship and rights recognition in relationship to land, water, and resources, we also extend that expression of stewardship and jurisdiction to digital society, because that knowledge also originates from our territories and peoples. It means embedding OCAP® and CARE principles (Collective Benefit, Authority to Control, Responsibility, and Ethics) directly into federal and provincial AI and data laws, ensuring that Indigenous frameworks guide how data is collected, stored, and used. It also means funding Indigenous-owned digital infrastructure, including community data centres, connectivity projects, and Indigenous-led AI research hubs; from design to delivery. These investments are not transactional, they are also part of the relationships we are building together and a demonstration of a commitment to justice, economic wellbeing and freedom, and reconciliation in this chapter of digital societies' evolution. DOI: 10.5281/zenodo.17328882 148
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 And finally, it means sharing power. Indigenous Nations must hold decision-making authority in Canada’s AI ethics councils, standards bodies, and policy design processes. True co-governance means designing systems together, from the beginning. These actions, along with the many offered by Indigenous peoples across the country, could provide a roadmap for practitioners and policymakers today. This could start with a simple shift in AI governance from risk management to relationality: a model that centers on consent, trust, and long-term accountability. Honesty and Hope The path forward requires bold and precise action. Building governance models that uphold this work and these principles will require governments to share authority, companies to shift business models, and institutions to learn and unlearn, in the pursuit of two-eyed seeing. The good news is, this work is already happening in communities, classrooms, and institutions everywhere. The important thing to remember is that Indigenous peoples have always been technologists. Our ancestors engineered governance systems, trade networks, and ecological knowledge systems throughout this land's most complex and evolutionary times of change. The same principles that designed and sustained those systems can also sustain this next evolution of technological systems change. This is truly a journey of shared liberation, if we choose to see it that way and invest in it accordingly. The digital world we are building will remember what we teach it. Let’s ensure it remembers respect, humility, and our commitment to life in all its forms. If we do that, AI can become not just intelligent, but wise. When we design and govern for seven generations, our technologies become good ancestors About the Author Denise Williams is a proud member of the Cowichan Tribes and former CEO of the First Nations Technology Council. A Dialogue Fellow at SFU’s Morris J. Wosk Centre for Dialogue, she leads national conversations on Indigenous digital sovereignty, AI governance, and economic well-being. Denise holds an MBA from SFU’s Beedie School of Business and works across universities, philanthropy, and government to build systems grounded in reciprocity, innovation, and Indigenous leadership that shape a more equitable future. Cite this Article Williams, D. (2025) Indigenous approaches to AI governance: data sovereignty, seven-generation thinking, and long-term stewardship. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 147-149. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 149
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 16.3 How Nonprofits Are Using AI: What’s Working, What’s Not, and What They Need to Succeed By Jenni Warren & Bryan Lozano, Tech:NYC Foundation When Dave Spencer at New York Sun Works trained an AI model to analyze program reports, he expected a small efficiency gain. Instead, the task that once consumed an entire workday now takes ten minutes. Across the social-impact field, similar experiments are transforming daily work. From museums generating personalized storybooks to libraries offering multilingual search, nonprofits are discovering that AI’s value lies in the quiet redesign of everyday tasks. Decoded Futures, a program of the Tech:NYC Foundation, has trained over a thousand nonprofit leaders in hands-on, practical AI applications. Its training approach combines technical learning with human-centered design, helping participants translate new tools into meaningful practice. Three key components make this learning stick: 1. In-person, exploration-based learning: Participants experiment with AI tools, connecting concepts directly to their own organizational challenges. 2. Tech expert support: Each cohort includes access to practitioners who can troubleshoot, recommend tools, and translate technical ideas into nonprofit contexts. 3. Workflow and problem mapping: Participants identify specific pain points first, then design clear workflows where AI can add measurable value. Alongside these practices, every project is grounded in three ethical guidelines that ensure responsible and inclusive use: 1. Personally Identifiable Information: Free AI tools aren’t private; never share sensitive or personal data unless it’s through a secure platform. 2. Keep a Human in the Loop: Always review AI outputs before relying on them or sharing externally. 3. Customize for Your Community: Out-of-the-box models rarely reflect every culture or experience; nonprofits should adapt AI to serve their specific audiences. Follow-up interviews reveal a clear pattern: AI has become a practical collaborator across fundraising, education, workforce development, and public service. 95% of alumni continued using AI after the program, building more than 200 new workflows. Their stories reveal what’s working, where friction remains, and what kind of support will determine whether this next wave of adoption advances equity or deepens divides. DOI: 10.5281/zenodo.17328882 150
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 What’s Working Everyday efficiency: AI is saving nonprofits time and mental bandwidth. At CUNY K16, Rachel Hutchins uses AI for charts and regression analyses that used to take hours. These changes shift effort away from repetitive work and toward mission-critical programs. Mission-aligned innovation: Organizations are using AI to enhance creativity and access. The Bronx Children’s Museum built a story-generation tool featuring Bronx kids, expanding literacy access. The Brooklyn Public Library launched a multilingual recommendation system across 60 branches. Both examples show AI applied with cultural sensitivity. From pilots to systems: Some nonprofits are embedding AI into core operations. At The Leadership Academy, Alexander Negron integrated AI into Salesforce and Monday.com to automate workflows and support staff learning. United Neighborhood Houses is building a dashboard linking 45 settlement houses, creating real-time visibility across 800,000 New Yorkers served. Internal capacity building: Adoption spreads fastest when someone inside champions it. At Berkeley College, Randy Gomez built a cross-department GPT to identify partnerships and trained colleagues. At CUNY K16, Rebecca Beeman trained 18 senior leaders and surveyed adoption rates to guide decisions. Such initiatives turn curiosity into institutional readiness. What’s Not Working ● Institutional hesitation: At larger organizations, some alumni use AI daily but face a system-wide moratorium. Similar caution across large institutions slows innovation until policy catches up, forcing some staff to innovate quietly. ● Resource constraints: Smaller nonprofits often lack the engineering support necessary to maintain their tools. Innovators easily build prototypes but struggle to sustain progress without technical mentorship or funding. ● Weak measurement: Few organizations formally track AI outcomes. Most rely on anecdotes, which makes it harder to evaluate the results. ● Fragmented support: Leaders describe needing continuing education, troubleshooting spaces, and policy guidance. They want peers to learn with, not just one-off training sessions to attend. What Nonprofits Need to Succeed The next phase of AI adoption depends on ecosystem design, which includes shared infrastructure, peer learning, and steady investment. ● Community of practice: Networks or spaces to exchange examples. Peer learning transforms scattered pilots into a collective force for progress. ● Hands-on guidance: Continued office hours or access to experts who can help implement ideas in real time. DOI: 10.5281/zenodo.17328882 151
Chapter 16: Civil Society and AI PART V | SAIER Vol. 7 | Nov 2025 ● Advanced training: Deep workshops on automation, workflow design, and ethics, especially those scaling from prototypes to systems. ● Organizational support: Writing AI policies, integrating tools, or connecting with funders. What nonprofits need most is patient capital for responsible scaling. Funders can meet these needs by investing in communities of practice, applied learning grants, and shared ethical infrastructure. The Path Forward AI in civil society is evolving from pilot projects into everyday infrastructure. The leaders driving this change are proving that innovation rooted in mission can thrive without massive budgets. Even as funding is pulled back across the US, nonprofits are learning to scale impact and reshaping the social sector with AI. What they need now is sustained funding, technical mentorship, and communities that foster continuous learning. That is how AI transforms the social sector, becoming a durable tool for equity and change. About the Authors Jenni Warren is the Program Director of Decoded Futures at Tech:NYC, leading efforts to equip nonprofits with the transformative power of AI. With over 15 years of experience in learning and development both internationally and domestically, she has built innovative programs at the intersection of education, technology, and equity. Jenni holds a masters degree in Early Childhood Education from The Ohio State University and is passionate about using tech for social impact. Bryan is the Director of the Tech:NYC Foundation. Bryan is leading the foundation to scale the organization’s K-12 CS education and workforce development initiatives and deepen the tech sector’s support of other economic development and social impact issues. Under Bryan's leadership, the Foundation has launched Decoded Futures, an initiative empowering NYC nonprofits with AI tools and expertise to enhance their social impact. A graduate of Stony Brook University and the NY Coro Fellows Program. Cite this Article Warren, J. & Lozano, B. (2025). How Nonprofits Are Using AI: What’s Working, What’s Not, and What They Need to Succeed. In R. Butalid, C. Wright, & I. Kherroubi García, (Eds.), The State of AI Ethics Report (Volume 7) - AI at the Crossroads: A Practitioner's Guide To Community-Centered Solutions. pp. 150-152. Montreal AI Ethics Institute. DOI: 10.5281/zenodo.17328882. Available at: https://montrealethics.ai/state. DOI: 10.5281/zenodo.17328882 152