Generative AI and the Urban AI Policy Challenges Ahead: Trustworthy for Whom?

1 Generative AI and the Urban AI Policy Challenges Ahead: Trustworthy for Whom? Abstract (250): This Special Issue of TGPPP addresses the accelerating convergence between GenAI, Urban AI, and public governance. As algorithmic systems are deployed across domains such as transport, welfare, housing, education, and security, cities become both laboratories of innovation and battlegrounds of democratic legitimacy. Trustworthy AI has emerged as a policy imperative—but the question remains: trustworthy for whom? Rather than treating trust as a static feature of technical systems, this Special Issue reframes it as a relational, contested, and institutionally mediated concept—fundamental to the legitimacy of public action in the digital age. Contributions are encouraged that explore GenAI not only as a technological advance, but as a governance challenge that reconfigures discretion, authority, and the social contract between citizens and institutions. Inspired by Richard R. Nelson’s enduring metaphor of “The Moon and the Ghetto”—which captures the asymmetries between technological sophistication and social equity—this Special Issue invites a renewed interrogation of how AI policy can bridge systemic inequalities rather than entrench them. Urban governance frameworks must move beyond techno-solutionism to embed pluralism, accountability, and public value at the core of GenAI deployments. Scholars, policymakers, and practitioners are invited to submit empirical, theoretical, and policy-oriented contributions that critically examine how Urban AI can be governed with legitimacy, reflexivity, and justice. Keywords (10): Gen AI, Urban AI, Trustworthy AI, Smart Cities, Innovation Systems and Institutions, Algorithmic Governance, Digital Inclusion, Applied Economics, Public Policy, Epistemic Authority Wordcount: 7779 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Calzada I (2025;), "Generative AI and the urban AI policy challenges ahead: Trustworthy for whom?". Transforming Government: People, Process and Policy. https://doi.org/10.1108/TG-08-2025-0240 © 2025 Emerald Publishing Limited. This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. Transforming Government: People, Process and Policy 2 Introduction to the Special Issue: Generative AI and the Urban AI Policy Challenges Ahead: Trustworthy AI for Whom? Richard R. Nelson’s enduring metaphor of The Moon and the Ghetto (1977, 2011) captures a paradox that continues to define contemporary policy dilemmas: while advanced societies have demonstrated the scientific and technical capacity to achieve extraordinary feats—such as landing on the moon—they still struggle to address persistent social injustices within their own cities. This paradox resonates powerfully in the current moment of generative and urban artificial intelligence (AI). As large-scale models and predictive algorithms increasingly shape public services, institutional decision-making, and urban governance, the same disjunction between technological capability and socio-political neglect becomes ever more acute. The promise of GenAI and Urban AI lies in optimization, personalization, and enhanced administrative efficiency; yet these capabilities risk being deployed without confronting deeper structural inequalities. The Special Issue addresses this core tension by asking: what does it mean to pursue “trustworthy AI” in a world still marked by deep civic distrust, opaque infrastructures, and asymmetrical access to digital rights? Urban contexts—where most AI experimentation takes place—provide a critical terrain for exploring this question. Rather than replicating moonshot ambitions, contributors are encouraged to interrogate the institutional, economic, and democratic conditions under which AI technologies are implemented, resisted, or reimagined. Trust, in this framework, becomes not a technical guarantee but a political achievement—contingent on legitimacy, inclusion, and social justice. Artificial Intelligence (AI) is no longer a futuristic aspiration or a marginal experiment—it has become a constitutive force in the governance of contemporary societies. This transformation is particularly pronounced in urban contexts, where the convergence of digital infrastructures, algorithmic decision-making, and civic life has turned cities into both laboratories of innovation and battlegrounds of governance (Author and Cobo, 2015; Visvizi and Lytras, 2019; Yigitcanlar et al., 2025). Today, with the explosive rise of Generative AI (GenAI), we are Page 2 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 3 entering a new phase of this evolution: one in which synthetic knowledge, probabilistic reasoning, and automated interaction are increasingly embedded into the very mechanisms by which public institutions function. Yet the arrival of GenAI also raises fundamental questions about trust, legitimacy, and democratic control. As this Special Issue provocatively asks: Trustworthy AI—for whom? (Author et al., 2025). Who defines the parameters of trustworthiness? Whose voices shape the governance of AI systems? And what institutional architectures are in place—or missing—to ensure that AI aligns with public values rather than market imperatives or technocratic abstraction? In the context of public governance, and especially within urban and regional domains, the concept of “trustworthy AI” is far from neutral. It carries deep normative weight, entangled with the challenges of institutional legitimacy, participatory inclusiveness, and technological opacity (Gangadharan, 2020). Rather than framing trust as a static attribute of systems, this Special Issue approaches it as a contested, negotiated, and relational dynamic—an outcome of socio-technical arrangements and political choices. This introduction thus serves as both a conceptual roadmap and a call to action. We invite scholars, policymakers, technologists, and civic actors to examine the social, political, and institutional reconfigurations ushered in by GenAI and Urban AI. We are particularly interested in contributions that interrogate how these technologies affect the ability of public institutions to govern in ways that are just, accountable, and inclusive (Author and Eizaguirre, 2025a–d). To do so, we must move beyond the familiar promises of efficiency and optimisation. We must critically assess how AI systems are embedded in bureaucratic routines, mediated by data infrastructures, and shaped by the power dynamics of their design and deployment (Barlow, 1996; Bigo et al., 2019; Buolamwini, 2024). GenAI does not merely automate tasks—it coproduces decisions, structures public meaning, and reconfigures the contours of the social contract. Trust, in this context, must be redefined not as mere acceptance of AI outputs but as Page 3 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 4 a relational and contested dynamic between citizens, institutions, and the opaque systems mediating them (Eubanks, 2018; van Dijck et al., 2025). The deployment of GenAI in cities risks reinforcing existing hierarchies of knowledge and control if it is not accompanied by institutional mechanisms for accountability, interpretability, and civic deliberation (Ulnicane et al., 2022; Gangadharan, 2017). This Special Issue is therefore an invitation to rethink what trustworthy AI might mean when placed in the hands of public institutions. Not simply whether AI is explainable or fair, but whether it is governed democratically, rooted in public oversight, and reflective of diverse civic imaginaries. We seek contributions that centre legitimacy, accessibility, and pluralism as foundational principles in the governance of GenAI in urban life. As such, we urge contributors to critically engage with the ways GenAI systems can recalibrate the social contract between the governed and governing—not only through technical design but through governance frameworks that center legitimacy, accessibility, and pluralism in cities (Author and Almirall, 2020). Beyond Technocratic Promises: Reclaiming Public Governance The common narrative—resonating with its cousin hype term, smart cities—around UrbanAI in public administration is infused with promises: increased efficiency, responsiveness, personalization, and optimization (Author and Cowie, 2017; Cardullo et al., 2019). Yet, these benefits are unevenly distributed, often masking or even reproducing structural inequalities (Author and Eizaguirre, 2025b). A more critical approach asks not merely how AI works, but for whom it works, under what conditions, and with what consequences. Rather than refining ethics checklists or pursuing technical robustness in isolation, this Special Issue calls for a deeper engagement with the democratic, civic, and institutional dimensions of GenAI and UrbanAI governance (Author, 2025a). This moment invites a rethinking of digital transformation not as a purely technological endeavor, but as a political and institutional project that must center public sovereignty, Page 4 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 5 democratic accountability, and collective agency (European Commission, 2025c). Recent and past policy frameworks—such as the European Commission’s AI Continent: Action Plan (2025b) and the European Economic and Social Committee’s Proposal for a Sovereign and Democratic Digitalisation (EESC, 2019)—signal an awareness of these imperatives. They call for governance models that safeguard not only innovation and competitiveness but also fundamental rights, interoperability, and civic participation. However, without a grounded implementation strategy that reclaims public governance as the core infrastructure for AI deployment, these frameworks risk becoming aspirational rather than operational (Author and Almirall, 2019a, 2019b). As GenAI systems are embedded into the everyday decision-making architectures of cities and institutions, we must critically examine who steers these transitions, whose values are encoded, and what accountability mechanisms are in place to prevent capture—either by markets or by opaque technocracies (Author, 2018). In this light, AI in governance must be re-situated not only as a technological or policy problem but as a political and social one. This means interrogating the ways in which power, voice, and representation are embedded—or excluded—from AI systems deployed by public institutions. Trustworthiness should be seen not as a static feature of a system but as a dynamic, negotiated outcome of institutional arrangements, political cultures, and civic interactions that could result in social innovations (Author, 2024). Cities as Laboratories and Battlegrounds The urban context amplifies these challenges and opportunities (Galceran-Vercher and Vidal D’oleo, 2024). As AI tools are increasingly integrated into transport systems, waste management, predictive policing, education, and healthcare planning, cities become laboratories of digital experimentation—but also battlegrounds of technological governance. The deployment of GenAI in these domains has the potential to transform how municipal decisions are made, how public services are delivered, and how citizens relate to their institutions (Visvizi and Lytras, 2018). Page 5 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 6 Alongside previous action research on 13 smart global cities conducted through the Cities Coalition for Digital Rights (Author et al., 2021) including Long Beach, Toronto, Porto, Amsterdam, London, Vienna, Milano, Los Angeles, Portland, San Antonio, New York, Barcelona and Glasgow; the Barcelona Centre for International Affairs (CIDOB) has recently examined six global cities—Barcelona, Amsterdam, New York, San José, Dubai, and Singapore—in its report IA urbana ética en la práctica (Galceran-Vercher and Vidal D’oleo, 2024). These cases shed light on the diverse governance models shaping how AI is deployed in urban settings and the varied interpretations of what constitutes "trustworthy" AI. The six case studies illustrate the diverse and contested trajectories of Urban AI deployments in global cities. These implementations offer insights into institutional legitimacy, technological opacity, governance models, and civic trust, as well as the uneven social consequences of datafication and AI-enhanced systems—especially as GenAI deepens these dynamics (Visvizi et al., 2025). Barcelona: From Digital Rights to Public Algorithmic Infrastructure Barcelona exemplifies a normative counter-model rooted in technopolitical sovereignty. With initiatives like DECIDIM, the city promotes public algorithmic accountability and citizen cogovernance. The deployment of AI is grounded in transparency, participatory ethics, and public-sector data infrastructures, aiming to contest the platformization of urban life. Trustworthiness for Whom? For citizens as political actors, not merely users—especially through decentralized deliberation frameworks and open-source civic tech. GenAI Risk: Even with ethical infrastructures, GenAI could recentralize power if co-opted by technocratic governance. Key Tension: Maintaining institutional transparency and civic agency while scaling AI complexity. Amsterdam: Negotiating Platform Governance and Public Values Amsterdam anchors its AI governance on public value orientation and regulatory experimentation, including the Tada Manifesto and the Algorithm Register. The city Page 6 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 7 collaborates with academic and civil society partners to ensure algorithmic transparency and auditability. Trustworthiness for Whom? Aims at inclusive urban citizenship, using data commons frameworks to balance private-sector influence. GenAI Risk: Vulnerable to regulatory capture by dominant tech firms despite strong civic coalitions. Key Tension: Translating principles into operationalized accountability mechanisms amid rising automation. New York: Institutionalizing AI Ethics in a Hyper-Datafied City New York positions itself as a global benchmark in regulatory foresight, launching tools like the Automated Decision Systems Task Force and the NYC AI Action Plan. The focus is on institutional transparency, especially regarding the social services impacted by AI (e.g., predictive policing, housing allocation). Trustworthiness for Whom? Attempts to safeguard vulnerable populations from discriminatory AI outcomes, especially in public services. GenAI Risk: The complexity of socio-technical systems could obscure public accountability, despite formal mechanisms. Key Tension: Ensuring equity and interpretability without reducing ethics to checkbox compliance. San José: Public-Private Technological Co-Governance in Silicon Valley San José, the capital of Silicon Valley, illustrates a technocratic governance model where AI experimentation is embedded in smart city infrastructure, often co-developed with tech giants. While innovation is rapid, civic deliberation mechanisms are limited. Trustworthiness for Whom? Trust is implicitly vested in technical competence and corporate innovation rather than democratic scrutiny. GenAI Risk: High potential for asymmetric knowledge power, where public institutions become dependent on private platforms. Key Tension: Lack of civic infrastructure to contest or negotiate AI outcomes, leading to potential alienation. Dubái: Hypermodernism and Authoritarian Technogovernance Dubái represents a hypermodern AI deployment strategy, with the state as a central orchestrator of a visionary smart city narrative. Trust is not deliberative but derived from state authority and Page 7 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 8 technological spectacle, exemplified by the Dubai AI Lab and the Smart Dubai Office. Trustworthiness for Whom? For the state as benevolent guardian of progress; citizens are conceptualized as beneficiaries, not agents. GenAI Risk: Consolidates opaque governance, with little to no institutional or civic oversight. Key Tension: Absence of participatory frameworks raises concerns of algorithmic authoritarianism. Singapore: Bureaucratic Techno-Solutionism with Strategic Foresight Singapore integrates AI through strategic techno-governance, emphasizing efficiency, safety, and planning precision. It combines state control with predictive analytics across healthcare, mobility, and education, under strong bureaucratic discipline. Trustworthiness for Whom? Trust is institutional, built on a paternalistic social contract and belief in data-driven competence. GenAI Risk: Risks reinforcing existing power asymmetries without room for dissent or pluralism. Key Tension: Navigating between hyper-rationality and democratic participation in algorithmic governance. Urban AI Governance as a Reconfiguration of the Urban Social Contract These six cities embody distinctive configurations of trust, institutional legitimacy, and civic inclusion in the age of Urban AI. Yet, the rise of GenAI intensifies all previous tensions: opacity, centralization, and representational inequality. As such, this comparative analysis reinforces the editorial claim: Trustworthy AI is not a technical property—it is a political achievement. Barcelona stands out for embedding algorithmic transparency and citizen participation at the heart of its governance model. Through open-source platforms such as Decidim and initiatives led by the Municipal Data Office, the city seeks to reclaim public control over digital infrastructures, presenting a bottom-up alternative to private-led smart city models. Trust, here, is relational and political—cultivated through deliberation and institutional openness. Amsterdam offers a similarly values-driven approach, pioneering tools like the Algorithm Register and co-producing the Tada Manifesto with civil society actors. These instruments aim Page 8 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 9 to codify transparency, equity, and inclusiveness into the fabric of urban AI policy, although operationalising these values remains a work in progress. In contrast, New York has pursued a more regulatory and audit-driven model. With initiatives such as the Automated Decision Systems Task Force and the AI Action Plan, the city attempts to navigate the opacity of algorithmic systems, particularly in domains like housing, policing, and social services. Yet the institutional burden of achieving accountability often rests on compliance mechanisms rather than civic co-governance. San José, situated in the epicentre of Silicon Valley, embodies the tensions between technological innovation and public accountability. While the city has partnered with tech firms to accelerate AI adoption in public administration, governance structures often lag behind, raising concerns about asymmetries in expertise, power, and oversight. Dubai and Singapore represent two state-centric approaches to Urban AI, albeit with different political textures. In Dubai, AI is deployed as part of a hypermodern national branding strategy, where the Smart Dubai agenda positions technology as a symbol of progress and efficiency. However, this model operates with limited civic scrutiny and assumes trust as a function of authority rather than deliberation. Singapore similarly integrates AI into its bureaucratic and technocratic planning frameworks, emphasising foresight, optimisation, and system-wide efficiency. Trust, in this context, is embedded in institutional legacy and state competence— but tends to exclude bottom-up accountability mechanisms. Taken together, these six cases illustrate that the governance of AI in cities is not merely a question of technological capacity or ethical intention, but one of institutional design, political culture, and civic imagination. As Generative AI becomes more embedded in urban decisionmaking, these existing governance models will be further tested. The question “trustworthy AI—for whom?” thus becomes not only a normative challenge but a practical one: how to ensure that algorithmic systems are not only technically sound but socially legitimate, publicly accountable, and democratically co-produced. Page 9 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 16 long emphasized by scholars such as Mazzucato (2013), Nelson and Winter (2006), and Polanyi (1944)—must be reasserted in AI governance, particularly at the municipal level. In the contemporary policy discourse, cities are increasingly portrayed as agile actors capable of deploying experimental technologies at scale. Yet without robust institutional architectures, these deployments risk replicating extractive models of surveillance capitalism and digital feudalism (Zuboff, 2019; Morozov, 2022). The challenge lies in creating governance systems that are not only adaptive and innovative but also inclusive, transparent, and democratically accountable. Drawing from Breznitz (2007), this Special Issue notes that institutional diversity and political agency are critical determinants of success in innovation policy. His comparative analysis of Israel, Taiwan, and Ireland underscores how national and regional institutions—when aligned with long-term strategic visions—can foster distinctive innovation outcomes. Similarly, cities must develop place-specific institutional responses to Urban AI, shaped by local needs, values, and capabilities as we examined with the cases of Barcelona, Amsterdam, New York, San José, Dubai, and Singapur (Author et al., 2021; Galceran-Vercher and Vidal D’oleo, 2024). One promising direction is the emergence of “public algorithmic infrastructures” (Author, 2026a), in which data governance, AI deployment, and civic engagement are integrated into the core functions of municipal governance. These infrastructures are not merely technical systems but also institutional frameworks that embed accountability, contestability, and coproduction into AI design and operation. In this sense, institutional architecture is both a material and normative structure—it enables action while shaping the very terms of democratic participation. From Entrepreneurial State to Entrepreneurial City Building on Mazzucato’s (2013, 2022) notion of the Entrepreneurial State, this Special Issue argues that cities must act as entrepreneurial agents in the governance of GenAI. This does not mean emulating private-sector logics of disruption or efficiency. Rather, it entails missionPage 16 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 17 oriented innovation strategies that align AI deployment with broader societal goals—equity, sustainability, democratic participation, and public value creation. Urban administrations can—and should—serve as orchestrators of innovation ecosystems, convening public, private, and civic actors to co-develop AI solutions that respond to shared challenges. This requires new institutional capacities, including foresight units, civic labs, data stewardship offices, and algorithmic audit mechanisms. These capacities are already emerging in pioneering municipalities such as Barcelona, Amsterdam, and Helsinki, where participatory frameworks, open-source platforms, and ethical AI guidelines are being operationalized (Jacobs, 1966). Yet these developments remain fragile. Without sustained political commitment and financial support, they risk being sidelined by techno-solutionist narratives or captured by market interests. The lesson from Breznitz and Zysman (2013) is instructive here: innovation ecosystems flourish when public institutions maintain strategic autonomy, invest in institutional capabilities, and actively shape market structures—not when they merely adapt to global trends. Embedding AI in Civic Economies: Rethinking Urban Value Creation The deployment of GenAI in cities raises critical questions about economic value—who creates it, who captures it, and how it is distributed. Mainstream economic models, such as those advanced by Porter (1985), focus on competitiveness and efficiency. But in the context of Urban AI, value must also be measured in terms of civic empowerment, democratic accountability, and social cohesion. Here, Polanyi’s (1944) concept of the embedded economy becomes relevant. AI governance cannot be abstracted from the social institutions, cultural norms, and political structures that give it meaning and legitimacy. Cities must therefore design AI systems that are embedded in local civic economies—not imposed through technocratic blueprints or global platforms. Page 17 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 18 This embeddedness also demands new forms of value accounting. Florida (2002) argued, probably not anticipating such disruption, that creativity and knowledge production might be central to urban economic growth. But in the age of GenAI, this creativity is increasingly automated, commodified, or outsourced. How can cities reclaim creative labor, ensure data sovereignty, and support collective intelligence in ways that resist enclosure? This is not merely a question of policy, but of institutional imagination. And definitely not a matter or creativity as Florida wrongly anticipated (Hallonsten, 2024). One pathway is through the development of data commons and cooperative infrastructures, such as DAOs and data trusts (Author, 2024; Nicole et al., 2025). These models offer alternatives to proprietary platforms by enabling collective data governance, shared ownership, and participatory control over AI systems. They also reconnect AI deployment to broader agendas of democratic innovation and urban justice. Towards Democratic AI Economies in Cities The future of Urban AI governance will be shaped not only by technological capabilities or policy choices but by the institutional architectures we build—or fail to build—today. Drawing from innovation systems theory, evolutionary economics, and political economy, this section has outlined a roadmap for rethinking AI economics in cities. This Special Issue has argued that cities must act not as passive adopters of GenAI but as strategic stewards of its development and deployment. This requires investing in institutional capacities, fostering mission-oriented innovation ecosystems, and embedding AI within civic economies that prioritize public value over private profit. Trustworthy Urban AI, in this vision, is not just a matter of algorithmic transparency or technical robustness. It is a political and institutional achievement—one that must be coproduced by citizens, public officials, researchers, and civic technologists (Levi, 2024). By grounding GenAI governance in inclusive innovation systems and robust institutional Page 18 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 19 architectures, we can begin to chart a path toward more just, democratic, and resilient urban futures. Urban AI and the Policy Design Challenge: Trust, Governance, and Applied Economics at the Edge of the GenAI Richard R. Nelson’s enduring metaphor of “the moon and the ghetto” (1977; 2011) captures a paradox at the heart of contemporary technological development (Ferrie, 2023): our societies possess the scientific and technical capacity to send a spacecraft to the moon, yet we struggle— or fail entirely—to address the systemic injustices that shape life in marginalized communities. As Nelson reminded us, the problem is not one of technical feasibility but of political will, institutional alignment, and public priority. In the age of GenAI, this paradox deepens. The moonshot ambitions of Urban AI promise optimization, automation, and hyper-efficiency in urban service delivery—from real-time mobility management to predictive welfare systems. But who defines these goals, and at what societal cost? As Hallonsten (2024) notes, today’s public policy discourse often celebrates “innovationism” as a technocratic fix-all, neglecting the structural inequalities that shape the lived realities of citizens—particularly in algorithmically governed cities. This section confronts that imbalance head-on. It invites researchers from applied economics, public policy, and AI governance to grapple with the mismatch between high-tech experimentation and low-trust public environments—between moonshots in algorithmic decision-making and persistent governance deficits in urban life. The challenge is not merely to make Urban AI work, but to make it matter—institutionally, democratically, and economically. Building on Nelson’s framework, this Special Issue argues that the promise of Urban AI will be hollow unless accompanied by a recalibration of governance architectures. Drawing on Schumpeterian insights (1943) and evolutionary economics (Nelson & Winter, 2006), the section situates GenAI not simply as a frontier of technical progress but as a public choice: Page 19 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 20 how innovation systems are structured, which missions are prioritized, and whose interests they serve. The real challenge, then, is not how we engineer Urban AI to achieve moonshot capabilities, but how we design inclusive, accountable, and coherent policies that bring these systems down to earth—into the messy, pluralistic, and contested terrain of the urban social contract. Concluding the Call: From the Ghetto to the City—Trustworthy AI as a Democratic Project To bring this editorial full circle, we return to Richard Nelson’s enduring provocation and to Jane Fountain’s (2022) recent and powerful revisitation of The Moon and the Ghetto. Fountain exposes how algorithmic systems—under the guise of neutrality and objectivity—can reproduce the very systemic injustices they purport to resolve (Author and Eizaguirre, 2025b). Her intervention challenges us to interrogate the mechanisms by which AI embeds bias, institutionalizes inequality, and limits recourse for those most affected by automated decisions. Fountain’s insight is crucial: the problem is not AI per se, but the societal structures it reflects and reinforces when left unexamined. Against this backdrop, trustworthy AI cannot be built through code alone (Monsees, 2019). It must be co-constructed through institutional trust, democratic accountability, and civic legitimacy. This is especially urgent in cities, where algorithmic systems increasingly intersect with complex issues of race, class, housing, policing, and welfare. If we fail to ask “trustworthy for whom?”, we risk repeating a long history of exclusion—this time, encoded in code. Over the past decade, I have had the privilege of contributing to these debates through research, teaching, and action at the University of Oxford and beyond, focusing on the intersection of AI, cities, and digital sovereignty. This Special Issue continues that commitment. It brings together interdisciplinary voices from across the globe to consider not only the capabilities of GenAI, but also its consequences for public life. At stake is nothing less than the future of democratic governance in a computational era. Page 20 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 21 We invite authors to join us in this collective effort. Let this not be another moonshot that bypasses the ghetto. Let this be a turning point where innovation meets inclusion—and where governance reclaims its rightful place at the centre of the algorithmic age. This Special Issue offers a space for rethinking GenAI not just as a technological artifact, but as a democratic project. Trustworthiness is not a fixed attribute but an ongoing achievement— one that depends on legitimacy, accountability, inclusiveness, and civic imagination. As researchers, policymakers, and citizens, we must ask not only what AI can do for governance, but also what kind of governance we want in an AI-mediated world. We welcome theoretical, empirical case studies, and practice-based submissions that grapple with these questions. Through this Special Issue, we aim to assemble a rich and diverse conversation that can inform policy, inspire action, and deepen our collective understanding of AI in public life. We thank all prospective authors for their insight, engagement, and commitment to transformative public sector innovation. We look forward to your contributions: https://www.emeraldgrouppublishing.com/calls-for-papers/generative-ai-andurban-ai-policy-challenges-ahead-trustworthy-ai-whom Page 21 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 22 REFERENCES Allen, D., Hubbard, S., Lim, W., Wagman, S., Zalesne, K. (2024). A Roadmap for Governing AI: Technology Governance and Power Sharing Liberalism. Harvard University Allen Lab for Democracy Renovation, Cambridge, MA, USA. Arendt, H. (1966). The Origins of Totalitarism. London: Penguin. Badawy, W. (2025). Algorithmic sovereignty and democratic resilience: Rethinking AI governance in the age of generative AI. AI and Ethics. https://doi.org/10.1007/s43681025-00739-z Barlow, J. P. (1996). A declaration of the independence of cyberspace. Available at: https://vimeo.com/111576518?ref=tw-v-share (Accessed 1 November 2023). Bigo, D., Isin, E. and Ruppert, E. (2019). Data Politics. London: Routledge. Breznitz, D. (2007). Innovation and the State: Political Choice and Strategies for Growth in Israel, Taiwan, and Ireland. New Haven and London: Yale University Press. Breznitz, D. (2021). Innovation in Real Places. Oxford: Oxford University Press. Breznitz, D. & Zysman, J. (2013). The Third Globalization: Can Wealthy Nations Stay Rich in the Twenty-First Century? Oxford: Oxford University Press. Bühler, M.M., Author, Cane, I., Jelinek, T., Kapoor, A., Mannan, M., Mehta, S., Mookerje, V., Nübel, K., Pentland, A., Scholz, T., Siddarth, D., Tait, J., Vaitla, B. & Zhu, J. (2023). Unlocking the Power of Digital Commons: Data Cooperatives as a Pathway for Data Sovereign, Innovative and Equitable Digital Communities. Digital, 3(3), 146–171. doi: 10.3390/digital3030011. Buolamwini, J. (2024). Unmasking AI. London: Random House. Author (2026a). Understanding AI Economics: Applied Economics, Public Policy and Digital Citizenship. Chentelham: Edward Elgar. Author (2026b). Benchmarking City-Regions. Oxon: Routledge. Author (2025a). Datafied Democracies & AI Economics Unplugged. Cham: Springer Nature. Author (2025b). Digital Inclusion and Generative AI: Towards a Socially Cohesive and Digitally Sovereign Gipuzkoa. International Summer School Keynote, Donostia-San Sebastián, Gipuzkoa. Zenodo DOI: 10.5281/zenodo.15873570 Author (2025c). From Data-opolies to Decentralization? The AI Disruption Amid the Web3 Promiseland at Stake in Datafied Democracies. In: Visvizi, A., Troisi, O., Corvello, V., Grimaldi, M. (eds) Research and Innovation Forum 2024. RIIFORUM 2024. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-03178623-5_26 Author (2024). Artificial Intelligence for Social Innovation: Beyond the Noise of Algorithms and Datafication. Sustainability 16, 8638. https://doi.org/10.3390/su16198638 Author (2021a), Smart City Citizenship, Cambridge, Massachusetts: Elsevier Science Publishing Co Inc. [ISBN (Paperback): 978-0-12-815300-0]. doi:10.1016/c2017-002973-7. Author (2021b). Data Co-operatives through Data Sovereignty. Smart Cities, 4(3), 1158–1172. doi: 10.3390/smartcities4030062 Author (2018). "(Smart) Citizens from Data Providers to Decision-Makers? The Case Study of Barcelona", Sustainability, Vol. 10 No. 9, 3252. doi:10.3390/su10093252 Author, Németh, G., and Al-Radhi, M.S. (2025). Trustworthy AI for Whom? GenAI Detection Techniques of Trust Through Decentralized Web3 Ecosystems. Big Data Cogn. Comput. 9, 62. https://doi.org/10.3390/bdcc9030062 Author and Eizaguirre, I. (2025a). Call for Papers: Digital Inclusion & Urban AI. Discover Cities. https://link.springer.com/collections/bhgeijbhib Page 22 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 23 Author and Eizaguirre, I. (2025b). Digital Inclusion & GenAI. Summer School, 15-16 July 2025: https://www.uik.eus/en/activity/digital-inclusion-generative-artificialintelligence-gipuzkoa-socially-cohesive-digitally Author & Eizaguirre I. (2025c) Gipuzkoa: Sozialki kohesionatua, baina digitalki jasangarria al da? Berria. Available at: https://www.berria.eus/iritzia/artikuluak/gipuzkoa-sozialkikohesionatua-baina-digitalki-jasangarria-da_2143482_102.html (Accessed 31 July 2025) Calzada I. & Eizaguirre I. (2025d) Gipuzkoa, ¿socialmente cohesionada, digitalmente sostenible? Diario Noticias de Gipuzkoa. Available at: https://www.noticiasdegipuzkoa.eus/opinion/2025/07/13/gipuzkoa-socialmentecohesionada-digitalmente-sostenible-9873647.html (Accessed 31 July 2025). Author and Almirall, E. (2020). Data Ecosystems for Protecting European Citizens’ Digital Rights, Transforming Government: People, Process and Policy (TGPPP) 14(2): 133147. DOI: 10.1108/TG-03-2020-0047. Author and Almirall, E. (2019a). "Barcelona’s Grassroots-led Urban Experimentation: Deciphering the ‘Data Commons’ Policy Scheme", in Data for Policy 2019, London. Zenodo. DOI:10.5281/zenodo.2604618. Author and Almirall, E. (2019b). "Is Barcelona establishing a sustainable paradigm in Europe by grassroots-led smart urban experimentation through the ‘data commons’ policy?" In the American Association of Geographers Annual Meeting 2019, Washington DC. Author and Cobo, C. (2015). "Unplugging: Deconstructing the smart city", Journal of Urban Technology, Vol. 22 No. 1, pp. 23-43. doi:10.1080/10630732.2014.971535. Author and Cowie, P. (2017). "Beyond Smart and Data-Driven City-Regions? Rethinking Stakeholder-Helixes Strategies", Regions Magazine, Vol. 308 No. 4, pp. 25-28. doi:10.1080/13673882.2017.11958675. Author, Pérez-Batlle, M., and Batlle-Montserrat, J. (2021). People-Centered Smart Cities: An exploratory action research on the Cities’ Coalition for Digital Rights. Journal of Urban Affairs, 45(9), 1537–1562. https://doi.org/10.1080/07352166.2021.1994861 Cardullo, P., Di Feliciantonio, C. and Kitchin, R. (2019). The Right to the Smart City. Bingley: Emerald. Castells, M. (1996). The Rise of the Network Society. London: Blackwell. Clark, A. (2025). Extending Minds with Generative AI. Nature Communications. DOI: 10.1038/S41467-025-59906-9. Couldry, N. & Mejias, U. (2024). Data Grab. Chicago: Chicago University Press. Engin, Z., & Treleaven, P. (2019). Algorithmic Government: Automating Public Services and Supporting Civil Servants in Using Data Science Technologies. The Computer Journal, 62, 448-460. https://doi.org/10.1093/comjnl/bxy082 Engin, Z. (2025, July 16). Symbiotic Public Systems: When neither humans nor AI can govern alone…. Data for Policy CIC - Zenodo. https://doi.org/10.5281/zenodo.16264090 Eubanks, V. (2017). Automating Inequality: How high-tech tools profile, police, and punish the poor. NYC: St. Martin’s Press. European Commission (2025a). The impact of human-AI interaction on discrimination (JRC139127). Publications Office of the European Union. https://data.europa.eu/doi/10.2760/0189570 European Commission (2025b). AI Continent: Action Plan, Publications Office of the European Union, Luxembourg. doi:10.2759/5570267 European Commission (2025c). EuroStack: An EU Stack for trustworthy AI and algorithmic systems – Towards a European ecosystem for socio-technical AI innovation [Pitch document]. Publications Office of the European Union. Page 23 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 24 European Economic and Social Committee (EESC) (2019). Proposal for a Sovereign and Democratic Digitalisation, Publications Office of the European Union, Luxembourg. DOI: 10.2864/314066 Ferrie, C. (2023). Quantum Bullsh*t: How to Ruin Your Life with Advice from Quantum Physics. NYC: Sourcebooks. Florida, R. (2002). The Rise of the Creative Class. NYC: Basic Books. Fountain, J. E. (2022). The moon, the ghetto and artificial intelligence: Reducing systemic racism in computational algorithms, Government Information Quarterly, 39, 2, 101645, ISSN 0740-624X, https://doi.org/10.1016/j.giq.2021.101645. Galceran-Vercher, M., & Vidal D'oelo, A. (2024). IA Urbana Ética en la Práctica: Mecanismos de Política para Establecer Marcos de Gobernanza Local. Monografías CIDOB 89. Barcelona: CIDOB. Available at: https://www.cidob.org/sites/default/files/202502/MONOGRAFIA%2089_CAST_2.pdf (Accessed 1 August 2025). Gangadharan, S. P. (2017). The downside of digital inclusion: expectations and experiences of privacy and surveillance among marginal internet users. New Media & Society, 19 (4). 597 - 615. ISSN 1461-4448 Gangadharan, S. P. (2020). Digital Exclusion: A Politics of Refusal. LSE Research Online. https://eprints.lse.ac.uk/103076/ Golumbia, D. (2024). Cyberlibertarianism. University of Minnesota Press. GovTechLab (2019). available at: www.govtechlab.org (accessed 1 March 2020). Gray, J., Gerlitz, C. and Bournegru, L. (2018). "Data infrastructures literacy", Big Data & Society, Vol. 5 No. 2, pp. 1-13. doi:10.1177/2053951718786316. Guggenberger, T. M., Schlueter Langdon, C., & Otto, B. (2025). Data spaces as metaorganisations. European Journal of Information Systems. https://doi.org/10.1080/0960085X.2025.2451250 Hallonsten, O. (2024). Innovationism and the New Public Intellectuals. In: Henrekson, M., Sandström, C., Stenkula, M. (eds) Moonshots and the New Industrial Policy. International Studies in Entrepreneurship, 56. Cham: Springer. https://doi.org/10.1007/978-3-031-49196-2_5 Helberger, N. & Diakopoulos, N. (2023). ChatGPT and the AI Act. Internet Policy Review, 12(1). doi:10.14763/2023.1.1682 Jacobs, J. (1961). The Death and Life of Great American Cities. London: Random House. Jasanoff, S. (2016). The Ethics of Invention: Technology and the Human Future. New York: Norton. Khan, K., Su, C.-W., Umar, M., & Zhang, W. (2022). Geopolitics of technology: A new battleground? Technological and Economic Development of Economy, 28(2), 442-462. doi: 10.3846/tede.2022.16028 Khanna, P. (2016). Connectography: Mapping the Global Network Revolution. NYC: Weidenfeld & Nicholson. Kitchin, R., Davret, J., Kayanan, C. M., & Mutter, S. (2025). Assemblage theory, data systems and data ecosystems: The data assemblages of the Irish planning system. Big Data & Society, 12(3). https://doi.org/10.1177/20539517251352822 (Original work published 2025) Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Mahomed, S., Wong, J., Waller, M., and Fischer, C. (2024). AI Explainability in Practice. The Alan Turing Institute. Levi, S. (2024). Digitalización Democrática. Barcelona: Rayo Verde. Lundvall, B.-A., Joseph, K.J., Chaminade, C. & Vang, J. (2009) Handbook of Innovation Systems and Developing Countries: Building Domestic Capabilities in a Global Setting. Chentelham: Edward Elgar. Page 24 of 26Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Transforming Government: People, Process and Policy 25 Lundvall, B.-Å. (1992). National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning. London: Pinter. Nabben, K. (2022). A political history of DAOs. Available at: https://www.fwb.help/wip/cypherpunks-to-social-daos (Accessed 1 November 2023). Nabben, K. Puspasari, N., Kelleher, M., & Sanjay, S. (2021). Grounding decentralised technologies in cooperative principles: What can ‘Decentralised Autonomous Organisations” (DAOs) and platform cooperatives learn from each other. Alternatives Data Futures: Cooperative Principles, Data Trusts, and the Digital Economy. The New School of Platform Cooperativism Consortium / Harvard Berkman Klein. 6th December 2021. Nelson, R.R. (1977). The Moon and the Ghetto: An Essay on Public Policy Analysis. New York: W.W. Norton & Company. Nelson, R. R. (2011). The Moon and the Ghetto revisited. Science and Public Policy, 38 (9), 681-690. DOI: 10.3152/030234211X13070021633404 Nelson, R. R. (1993). National Innovation Systems: A Comparative Analysis. Oxford: Oxford University Press. Nelson, R. R., & Winter, S. G. (2006). An Evolutionary Theory of Economic Change. Harvard: Belknap Press. Nicole, S., Vance-Law, S., Spelliscy, C., Bell, J. (2025, January). Towards Data Cooperatives for a Sustainable Digital Economy. Project Liberty Institute & Decentralization Research Center. Mazzucato, M. (2013). The Entrepreneurial State. London: Anthem Press. Mazzucato, M. (2022). Mission Economy: A Moonshot Guide to Changing Capitalism. London: Penguin. Monsees, L. (2019). Crypto-Politics: Encryption and Democratic Practices in the Digital Era. Oxon: Routledge. Morozov, E. (2022). Critique of techno-feudal reason. New Left Review. 133-134. 89–126. Polanyi, K. (1944). The Great Transformation: The Political and Economic Origins of Our Time. Boston: Beacon Press. Porter, M. (1985). The Competitive Advantage of Nations. NYC: Free Press. Ruppert, E., Isin, E. and Bigo, D. (2017). "Data politics", Big Data & Society, 1-7. doi:10.1177/2053951717717749. Schumpeter, J. A. (1943). Capitalism, Socialism and Democracy. Oxon: Routledge. Sieker, F., Tarkowski, A., Gimpel, L. and Osborne, C. (2025). Public AI-White Paper. Bertelsmann Stiftung: Gütersloh. Ulnicane, I., Knight, W., Leach, T., Stahl, B. C., & Wanjiku, W.-G. (2022). Governance of Artificial Intelligence: Emerging International Trends and Policy Frames. In M. Tinnirello (Ed.), The Global Politics of Artificial Intelligence (pp. 29–48). London: Routledge. https://doi.org/10.1201/9780429446726-2 van Dijck, J., van Es, K., Helmond, A. and van der Vlist, F. (Eds.) (2025). Governing the Digital Society: Platforms, Artificial Intelligence, and Public Values, Amsterdam University Press, Amsterdam. https://doi.org/10.5117/9789048562718 Visvizi, A., Kozlowski, K., Author, & Troisi, O. (2025). Multidisciplinary Movements in AI and Generative AI: Society, Business, Education. Chentelham: Edward Elgar. https://www.e-elgar.com/shop/gbp/multidisciplinary-movements-in-ai-andgenerative-ai-9781035358656.html Visvizi, A. and Lytras, M. (2018). "It's Not a Fad: Smart Cities and Smart Villages Research in European and Global Contexts." Sustainability, Vol. 10 No. 8, pp. 2727. doi: 10.3390/su10082727. Visvizi, A. and Lytras, M. (2019). "Reflecting on oikos and agora in smart cities context: concluding remarks", in Visvizi, A. and Lytras, M.D. (Eds.), Smart Cities: Issues and Page 25 of 26 Transforming Government: People, Process and Policy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60