Proposes the REAL framework for ML systems requirements engineering that weaves data/model/system requirements, uses failure-driven exploration, and supports iterative traceable refinement, shown via an autonomous driving example.
Towards best practices in AGI safety and governance: A survey of expert opinion
3 Pith papers cite this work, alongside 15 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.
The paper categorizes sources of catastrophic AI risks into malicious use, AI race, organizational risks, and rogue AIs, providing illustrative stories and mitigation suggestions for each.
citing papers explorer
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From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems
Proposes the REAL framework for ML systems requirements engineering that weaves data/model/system requirements, uses failure-driven exploration, and supports iterative traceable refinement, shown via an autonomous driving example.
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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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An Overview of Catastrophic AI Risks
The paper categorizes sources of catastrophic AI risks into malicious use, AI race, organizational risks, and rogue AIs, providing illustrative stories and mitigation suggestions for each.