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Comparison requires valid measurement: Rethinking attack success rate comparisons in AI red teaming
Alexandra Chouldechova, A. Feder Cooper, Solon Barocas +3
We argue that conclusions drawn about relative system safety or attack method efficacy via AI red teaming are often not supported by evidence provided by attack success rate (ASR)…
Bridging Prediction and Intervention Problems in Social Systems
Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou +32
Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals…
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen +34
"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyri…
Validating LLM-as-a-Judge Systems under Rating Indeterminacy
Luke Guerdan, Solon Barocas, Kenneth Holstein +3
The LLM-as-a-judge paradigm, in which a judge LLM system replaces human raters in rating the outputs of other generative AI (GenAI) systems, plays a critical role in scaling and st…
A Framework for Evaluating LLMs Under Task Indeterminacy
Luke Guerdan, Hanna Wallach, Solon Barocas +1
Large language model (LLM) evaluations often assume there is a single correct response -- a gold label -- for each item in the evaluation corpus. However, some tasks can be ambiguo…
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification
A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi +6
Variance in predictions across different trained models is a significant, under-explored source of error in fair binary classification. In practice, the variance on some data examp…