RiskNet: A large-scale dataset of AI risk incidents from news with alignment and multi-dimensional annotations
arXiv:2606.08376v1 Announce Type: new Abstract: As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity. Although existing governance frameworks articulate high-level principles for responsible AI, large-scale empirical resources for tracking and analyzing real-world AI risk incidents remain limited. Existing incident collections are often manually curated, relatively small in scale, and insufficient for continuous, data-driven monitoring and downstream computational
The increasing deployment of AI systems across critical domains has led to a natural rise in reported harms and failures, necessitating better empirical tracking.
A large-scale, multi-dimensional dataset of AI risk incidents provides critical infrastructure for developing more robust governance, regulation, and ethical deployment strategies for AI.
The availability of a comprehensive dataset like RiskNet enables data-driven analysis of AI risks, moving beyond anecdotal evidence or small-scale studies.
- · AI safety researchers
- · Policy makers
- · AI governance organizations
- · Responsible AI developers
- · AI developers ignoring safety
- · Organizations with opaque AI systems
Researchers gain a powerful tool to analyze and categorize AI failures.
This improved understanding could lead to more effective regulatory frameworks and industry best practices.
Enhanced public trust in AI systems could accelerate adoption in critical sectors, provided risks are demonstrably managed.
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Read at arXiv cs.LG