250 citations · 366 across the 47 of their papers we have counts for
4 papers · 1 filter
AI Exposure Scores: what they measure, what they miss, and what comes next
Campbell Lund, Thomas Euyang, Zanele Munyikwa +1
A set of exposure scores calculated in 2023 has become a central empirical input to the future of work debate. Produced by Eloundou et al. (2023) and referred to here as the GPTs a…
CIRCLE: A Framework for Evaluating AI from a Real-World Lens
Reva Schwartz, Carina Westling, Morgan Briggs +12
This study proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI system outcomes in deployment. Current…
The Multilingual Divide and Its Impact on Global AI Safety
Aidan Peppin, Julia Kreutzer, Alice Schoenauer Sebag +13
Despite advances in large language model capabilities in recent years, a large gap remains in their capabilities and safety performance for many languages beyond a relatively small…
The Leaderboard Illusion
Shivalika Singh, Yiyang Nan, Alex Wang +10
Measuring progress is fundamental to the advancement of any scientific field. As benchmarks play an increasingly central role, they also grow more susceptible to distortion. Chatbo…