Publications (8)
AI-Assisted Systematization for Evaluating GenAI Systems
Dhruv Agarwal, Emily Sheng, Chad Atalla +6
Evaluating generative AI (GenAI) systems is challenging because many targets of evaluation are broad, contested concepts, such as "reasoning," "fairness," or "creativity." When the…
A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications
Ahmed Magooda, Alec Helyar, Kyle Jackson +14
We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automa…
Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, A. Feder Cooper +17
The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as "a tangle of sloppy tests [and] a…
A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts
Alexandra Chouldechova, Chad Atalla, Solon Barocas +11
The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard…
Learning to see people like people
Amanda Song, Linjie Li, Chad Atalla +1
Humans make complex inferences on faces, ranging from objective properties (gender, ethnicity, expression, age, identity, etc) to subjective judgments (facial attractiveness, trust…
Taxonomizing Representational Harms using Speech Act Theory
Emily Corvi, Hannah Washington, Stefanie Reed +9
Representational harms are widely recognized among fairness-related harms caused by generative language systems. However, their definitions are commonly under-specified. We make a…