How FAIR-R Is Your Data? Enhancing Legal and Technical Readiness for Open and AI-Enabled Reuse

Title: How FAIR-R Is Your Data? Enhancing Legal and Technical Readiness for Open and AI-Enabled Reuse Authors: Katharina Miller, Vanessa Guzek (Miller International Knowledge, MIK), partner in Horizon Europe project IP4OS Conference: Open Science Conference 2025, Hamburg Description :This contribution, to be presented at the Open Science Conference 2025 in Hamburg, introduces the concept of FAIR-R as an evolution of the FAIR data principles (Findable, Accessible, Interoperable, Reusable). While FAIR focuses on technical openness, FAIR-R adds a crucial dimension: datasets must also be Responsibly licensed and legally ready for reuse in artificial intelligence (AI) and machine learning workflows. The presentation provides: A quick overview of FAIR vs. FAIR-R. Key licensing red flags that block reuse, such as missing licenses, NonCommercial (NC) or NoDerivatives (ND) clauses, or lack of machine-readable metadata. Common AI-specific barriers, including sensitive data, restrictive license clauses, proprietary formats, and insufficient traceability. Participants of the session applied a lightweight FAIR-R checklist to evaluate real datasets, identifying both technical and legal gaps that limit responsible reuse. The outcomes contribute to improving dataset readiness for Open Science and AI-driven research, offering practical guidance for researchers, institutions, and policymakers. Funding Acknowledgment:This work is part of the Horizon Europe project IP4OS (Grant Agreement No. 101188026), funded by the European Union. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EU nor REA can be held responsible for them.