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

2025-02-26

arXiv.org

AI Summary

Rapid advances in generative AI have exposed interoperability limitations in existing AI risk classification frameworks, hindering cross-stakeholder governance collaboration. To address this, we propose the first ontology-driven unified AI risk taxonomy, enabling semantic alignment of heterogeneous risk definitions, evaluation benchmarks, datasets, and mitigation strategies via a structured knowledge graph. Our approach integrates formal ontology modeling, AI-assisted compliance workflows, and an open-source toolchain—Risk Atlas Nexus—to automate risk identification, prioritization, and policy implementation. Key contributions include: (1) a standardized cross-framework risk mapping protocol; (2) a scalable, verifiable governance knowledge infrastructure grounded in formal semantics; and (3) a substantial reduction in the operational barrier to AI governance, empowering researchers, practitioners, and policymakers to collaboratively mitigate emerging generative AI risks and advance responsible AI at scale.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Knowledge Representation and Reasoning: Ontologies
• Humans and AI: AI for Accessibility

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Lack of interoperability between AI risk taxonomies hinders governance operationalization
• Need for consolidated AI risk classification aligned with governance frameworks
• Absence of tools bridging risk definitions, benchmarks, and mitigation strategies

Innovation

• Structured taxonomy consolidates diverse AI risks
• Open-source tools bridge risk definitions and mitigation
• Ontologies and knowledge graphs facilitate risk management