AccountabilitySafetyTransparencycs.CY
AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources
Frank Bagehorn, Kristina Brimijoin, Elizabeth M. Daly, Jessica He, Michael Hind, Luis Garces-Erice, Christopher Giblin, Ioana Giurgiu, Jacquelyn Martino, Rahul Nair, David Piorkowski, Ambrish Rawat, John Richards, Sean Rooney, Dhaval Salwala, Seshu Tirupathi, Peter Urbanetz, Kush R. Varshney, Inge Vejsbjerg, Mira L. Wolf-Bauwens
February 26, 2025
arXiv.org
14 citations
Key Takeaways
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Fragmented risk frameworks are hindering AI governance: The paper identifies that while many AI risk taxonomies exist, they don't work well together, making it difficult for organizations and policymakers to actually implement responsible AI practices. The AI Risk Atlas addresses this by consolidating these scattered frameworks into one structured system.
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A unified taxonomy can accelerate responsible AI adoption: By aligning AI risks with governance frameworks and creating open-source tools (the Risk Atlas Nexus), the authors demonstrate that organizations can more easily identify, prioritize, and address AI risks—lowering the practical barriers to implementing responsible AI.
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Knowledge graphs and automation can make AI governance more accessible: The framework uses ontologies and knowledge graphs to connect risk definitions with benchmarks, datasets, and mitigation strategies, suggesting that technology itself can help automate compliance workflows and make responsible AI practices more feasible for organizations of varying sizes.
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Collaboration across disciplines is essential for keeping pace with AI advancement: The paper emphasizes that responsible AI governance requires contributions from researchers, practitioners, and policymakers working together, and that this cross-domain collaboration is necessary because generative AI risks are evolving faster than any single group can address alone.