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AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources

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arxiv 2503.05780 v2 pith:2FQYIQHE submitted 2025-02-26 cs.CY cs.HC

Frank Bagehorn , Kristina Brimijoin , Elizabeth M. Daly , Jessica He , Michael Hind , Luis Garces-Erice , Christopher Giblin , Ioana Giurgiu
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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
This is my paper · ORCID
classification cs.CYcs.HC
keywords riskrisksatlasgovernanceframeworksmitigationopen-sourcestrategies
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack of interoperability between them creates challenges for researchers, practitioners, and policymakers seeking to operationalise AI governance. To address this gap, we introduce the AI Risk Atlas, a structured taxonomy that consolidates AI risks from diverse sources and aligns them with governance frameworks. Additionally, we present the Risk Atlas Nexus, a collection of open-source tools designed to bridge the divide between risk definitions, benchmarks, datasets, and mitigation strategies. This knowledge-driven approach leverages ontologies and knowledge graphs to facilitate risk identification, prioritization, and mitigation. By integrating AI-assisted compliance workflows and automation strategies, our framework lowers the barrier to responsible AI adoption. We invite the broader research and open-source community to contribute to this evolving initiative, fostering cross-domain collaboration and ensuring AI governance keeps pace with technological advancements.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits

    cs.CY 2026-07 unverdicted novelty 6.0 of 10

    The Eticas AI Risk Taxonomy v2.0.0 organizes 76 risk subcategories across 10 categories and demonstrates operationalization by measuring PII disclosure in GPT-4-0314 at 0%, 51%, and 84% under increasing adversarial co...

  2. PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation

    cs.HC 2026-01 conditional novelty 6.0 of 10

    PASTA is a model-card-based LLM pipeline that evaluates an AI system against five regulations in minutes for about $3, with expert-aligned violation and relevance scores.

  3. Rethinking Query Optimization for Multi-Agent Systems [Vision]

    cs.DB 2025-12 conditional novelty 6.0 of 10

    Agentic data pipelines are built by hand today; this paper sets a research agenda for automatically optimizing their structure, model choices, and execution engines jointly as a new query-optimization problem.

  4. Developing a Risk Identification Framework for Foundation Model Uses

    cs.CR 2025-06 conditional novelty 5.0 of 10

    The paper derives four design requirements for use-based foundation model risk identification and presents an initial questionnaire-based framework demonstrated on a visitor-center chatbot example.

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