15 citations · 24 across the 6 of their papers we have counts for
4 papers · 1 filter
Ask What Your Country Can Do For You: Towards a Public Red Teaming Model
Wm. Matthew Kennedy, Cigdem Patlak, Jayraj Dave +10
AI systems have the potential to produce both benefits and harms, but without rigorous and ongoing adversarial evaluation, AI actors will struggle to assess the breadth and magnitu…
Cascade! Human in the loop shortcomings can increase the risk of failures in recommender systems
Wm. Matthew Kennedy, Nishanshi Shukla, Cigdem Patlak +5
Recommender systems are among the most commonly deployed systems today. Systems design approaches to AI-powered recommender systems have done well to urge recommender system develo…
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…
International AI Safety Report
Yoshua Bengio, Sören Mindermann, Daniel Privitera +93
The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by…