Empirical analysis of 1,524 AI incident reports shows 83% arise from worker-AI trait misalignments, with 74% of those traceable to developers prioritizing efficiency over precision or personalization.
2025.Towards a common reporting framework for AI incidents
3 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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Delphi study of 272 experts finds 18 of 24 AI risks >10% likely to cause catastrophe by 2030 in business-as-usual, dropping to five under mitigations; users and public most vulnerable, developers and governments most responsible.
Frontier AI safety policies have a structural coordination gap caused by diffuse benefits and concentrated costs, which can be addressed by adapting precommitment and shared response protocols from other high-risk domains.
citing papers explorer
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The Quiet Path from Seemingly Minor Design Errors to Workplace AI Incidents
Empirical analysis of 1,524 AI incident reports shows 83% arise from worker-AI trait misalignments, with 74% of those traceable to developers prioritizing efficiency over precision or personalization.
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Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Delphi study of 272 experts finds 18 of 24 AI risks >10% likely to cause catastrophe by 2030 in business-as-usual, dropping to five under mitigations; users and public most vulnerable, developers and governments most responsible.
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The coordination gap in frontier AI safety policies
Frontier AI safety policies have a structural coordination gap caused by diffuse benefits and concentrated costs, which can be addressed by adapting precommitment and shared response protocols from other high-risk domains.