8 papers
A pragmatic classification framework for AI incident monitoring
Isaak Mengesha, Branwen Owen, Charlie Collins +4
Incident monitoring can drive safety improvements in high-reliability industries and population-scale technologies, but remains underdeveloped in AI governance. Public databases ca…
FLARE-AI: Flaw Reporting for AI
Shayne Longpre, Elaine Zhu, Carson Ezell +15
Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify…
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
AI Incident Monitoring through a Public Health Lens
Sophia Abraham, Taiye Chen, Cyril Chhun +5
Artificial intelligence systems are now deployed at scale across sectors, accompanied by a growing number of real-world incidents ranging from misinformation and cybercrime to auto…
Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
Sean McGregor, Victor Lu, Vassil Tashev +8
Large language model (LLM) benchmarks inform LLM use decisions (e.g., "is this LLM safe to deploy for my use case and context?"). However, benchmarks may be rendered unreliable by…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…