Bridging AI international policy and practice: the AIDV approach

11/26/25 1 1 Bridging AI international policy and practice AIDV WG Francis Crawley, Natalie Meyers, Rodrigo Roa, Seonyoung Kim, Patricia Buendia, Madhava Jay Oct 15, 25th RDA Plenary Meeting Brisbane, 2025 https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/activity/ 2 11/26/25 2 Acknowledgement of Country We acknowledge and celebrate the First Australians on whose traditional lands we meet, and we pay our respect to their elders past and present. 3 Welcome to new RDA members! OPENNESS COMMUNITYDRIVEN CONSENSUS NON-PROFIT AND TECHNOLOGYNEUTRAL HARMONISATION INCLUSIVITY 6 Guiding Principles are at the heart of the RDA community. JOIN THE RDA www.rd-alliance.org/register/ All RDA members are expected to adhere by the RDA Code of Conduct to foster a welcoming and inclusive environment. https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/activity/ 4 11/26/25 3 EOSC-Future/RDA AIDV Working Group and its core outputs Co-Chairs: Natalie Meyers & Francis Crawley https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/activity/ 5 Four Recommendations from the AIDV Working Group: Shaping Responsible AI https://www.rd-alliance.org/news/shaping-responsible-ai-four-new-recommendations-from-the-aidv-working-group Outputs •Guidance for Ethics committees & IRBs reviewing AI and DV projects •Guidance for Informed Consent in context of AI and DV •Secure Processing Environments for Open Science: Proposing Legal Foundations •AI Bill of Rights Recommendation Supporting/Other Outputs • Geographies of Trust: AI, Biomedicine, and the Next Era of Federated and Visiting Data Models whitepaper •AIDV-WG Shared Citation Library 11:35 6 11/26/25 4 The Shifting Paradigm: From Data Transfer to Data Visitation •Increasing volume and sensitivity of data (e.g., health, genomics). •Challenges of traditional data transfer (security, governance, duplication). •Data visitation: Enabling analysis in situ without physical movement. •Potential benefits: Collaboration, security, reduced burden on data owners. 11:40 7 DV4RDA TIGER Project: Putting RDA Principles into Practice Natalie Meyers 11:45 This DV4RDA project has received funding through RDA TIGER from the European Union’s Horizon Europe framework programme under grant agreement No. 101094406. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or institutions represented here. Neither the European Union nor the institutions can be held responsible for them. 8 11/26/25 5 Bridging Data Silos and Privacy •The Challenge: Biomedical data holds profound potential, but is often trapped in silos due to complex security and privacy concerns (e.g., HIPAA, GDPR). •The Solution: The DV4RDA project directly aligns with RDA's mission to champion interoperability, reproducibility, and accessibility by providing a practical framework to address these hurdles. 9 DV4RDA: Embodiment of RDA Core Principles •Community-Driven: Global RDA Members collaborated to implement policies and evaluate real-world data technology. •Delivering Value: The project champions producing tangible, implementable outputs through a practical data platform. •Harmonization: The project’s "computation-to-data" model aligns with data security and health data RDA working groups like: •Trusted Research Environments for Sensitive or Confidential Data •Health Data Commons GORC Profile •Building Immune Digital Twins 10 11/26/25 6 Goals of the DV4RDA project •Framework for Implementing Data Visitation technologies •Demonstrate and Evaluate Data Visitation in Practice •Enhance Data Security and Privacy •Promote FAIR Data Principles •Accelerate Research •Foster Global Collaboration 11 Participants' Recruitment 12 11/26/25 7 DV4RDA Participants 13 Actionable RDA Outputs •Policy Language: Proposed expansion of language for IRB Protocols and Informed Consent Forms (ICFs) to explicitly include "Data Visitation" as a protection method. •Security Framework: Developed a comprehensive "RDA AIDV Template -System Security Assessment Plan" for future Data Visiting Technologies. •Technical Tool: Implemented per-subject consent checking during the data quality control (QC) process for DV4RDA. •Bill of Rights: Expanded online documentation with AIDV policy information. 14 11/26/25 8 4 Short Talks Presenters: Rodrigo Roa, Seonyoung Kim, Patricia Buendia, Madhava Jay 15 Policy into Practice & the Data Observatory Experience Rodrigo Roa, Executive Director Data Observatory Santiago, Chile 11:50 16 11/26/25 9 Who we are? Data Observatory (DO) is a non-profit public– private–academic institution created by the Government of Chile, Amazon Web Services (AWS), and Adolfo Ibáñez University. Its mission is to acquire, process, and make available large volumes of data with scientific, technological, and social impact. 17 Astronomy Earth Observation Natural Resources Society Research & Work Areas Open data platforms and infrastructures with a FAIR approach (Findable, Accessible, Interoperable, Reusable), developed in collaboration and partnership with public and private institutions. 18 11/26/25 16 Per-Subject Informed Consent Verification During QC ●Built on the dbGaP model ●Standardized using the Informed Consent Ontology (ICO) 31 Implementation and Call to Action •IRB and ICF language now provide a clear framework for compliant Data Visitation •Institutions can adopt this language to demonstrate privacy-by-design practices •Aligns with NIH DMS & GDS policies, HIPAA security standards, and GDPR principles •Enables ethical AI applications while preserving participant autonomy •Call to Action: We invite RDA members and institutions to adopt and reference this guidance in their own policies and protocol templates “A reconsideration of the classic form of informed consent is necessary in light of AI. We need to support autonomy through practical, flexible consent mechanisms.” –Dr. Kristy Hackett, Institue on Ethics & Policy for Innovation, McMaster University 32 11/26/25 17 Questions for Seonyoung Kim about Informed Consent in DV? https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/outputs/ 12:20 33 Use Case: FAIRlyz Implementation of AIDV Policies Patricia Buendia 12:25 34 11/26/25 18 2. FAIRlyz Demo of New Features 3:25 minutes video 1. Video Slide Deck Presentation 2.49 minutes video Video Presentations Also in YouTube Also in YouTube 35 FAIRlyz: An Infrastructure for Secure Data Visitation *FAIR data is Findable, Accessible, Interoperable, Reusable *FAIRLYZ adds anaLYZable as a 5th principle to the FAIR principles *Data visitation refers to moving the analysis to the data Manage Data simply and securely + Validate Data with semi-automated QC through data visitation + Share Data based on FAIR principles 36 11/26/25 19 FAIRLYZ:Rethinking the Data Workflow •EHRs •Omics •Public Repositories •Researchers Data Consumer via Data Visitation QC Reports Study Data Registry Ontology and Omics models •Research Institutions •Funding Agencies •Collaborators •AI Access the registry to review Public or Private Data Curation Provide QC Score Data Contributor 37 Value Proposition: QC Before Commitment •The Pain Point: Researchers invest time and resources navigating DUA, IRB, and downloads only to discover unusable data. Meanwhile, repositories bear the cost of storing and maintaining access portals for datasets that never get used. •Our Solution: FAIRlyz QC allows data sharers to run in-place QC and share results before legal steps or repository uploads, aiding them with data curation. •The Result: Transparency ensures QC at the source, saving time and speeding scientific discovery. Repositories wait and accept only QC-ed data. 38 11/26/25 20 Future Focus AI-Powered QC New UI will integrate an AI agent chatbot to guide researchers through complex QC tasks intuitively. Enhanced Security Transitioning to a locally trained AI model to eliminate reliance on external APIs, ensuring data remains isolated and secure. Ecosystem Growth Release of opensource plugins and integration with federated learning networks to encourage community contribution and maximum extensibility. 39 Call to Action Seeking Institutional Data Owners Worldwide: •Validation: Help us ensure FAIRlyz meets real-world institutional needs •Customization Funding: Support tailored enhancements that reflect your unique data challenges. Seeking Developers: •Developer collaborators for our upcoming open-source plugin development Seeking Researchers: •Partners to test our new AI-powered QC features. 40 11/26/25 21 Questions about FAIRlyz Implementation about DV Policies? https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/outputs/ 12:35 41 Lightning Talk SyftBox: a General Purpose Solution for Data Visitation and Equitable Data Sharing Madhava Jay 12:40 42 11/26/25 22 Madhava Jay 🧬Rare Disease Patient Software Engineer @ OpenMined 🚀Help solve data access with open source 🌏Brisbane, Australia 📧[email protected] 43 Mission Building the public network for non-public information 44 11/26/25 23 -Founded in 2017 -Tech Nonprofit and 501(c)(3) -We build open-source privacy-preserving technologies -> 30 Team Members -> 230 GitHub Repos -~ 20k Slack Community 45 46 11/26/25 24 47 This lightning talk 1. Problems with data sharing 2. A general purpose solution 3. A use-case for equitable genomics 48 11/26/25 25 Data’s true power comes from collaboration. But many data owners are forced to choose between giving up data ownership through copying and centralization, or simply not participating. Due to legal and ethical constraints, copying data across borders is often unacceptable; resulting in no action. We need a new way to collaborate fairly and securely. The Motivating Problem 49 Remotely study data on a computer at another organisation Data Scientist Datasite Can answer a “specific” question …and only that question Retains governance over the information they steward …and never shares a copy of the data Data Visitation 50