6 papers
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…
Take Goodhart Seriously: Principled Limit on General-Purpose AI Optimization
Antoine Maier, Aude Maier, Tom David
A common but rarely examined assumption in machine learning is that training yields models that actually satisfy their specified objective function. We call this the Objective Sati…
Robustness tests for biomedical foundation models should tailor to specifications
R. Patrick Xian, Noah R. Baker, Tom David +5
The rise of biomedical foundation models creates new hurdles in model testing and authorization, given their broad capabilities and susceptibility to complex distribution shifts. W…
LLM Robustness Leaderboard v1 --Technical report
Pierre Peigné - Lefebvre, Quentin Feuillade-Montixi, Tom David +1
This technical report accompanies the LLM robustness leaderboard published by PRISM Eval for the Paris AI Action Summit. We introduce PRISM Eval Behavior Elicitation Tool (BET), an…
Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects
Reva Schwartz, Rumman Chowdhury, Akash Kundu +17
Conventional AI evaluation approaches concentrated within the AI stack exhibit systemic limitations for exploring, navigating and resolving the human and societal factors that play…
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…