2 citations · 3 across the 5 of their papers we have counts for
6 papers
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)…
Anecdoctoring: Automated Red-Teaming Across Language and Place
Alejandro Cuevas, Saloni Dash, Bharat Kumar Nayak +2
Disinformation is among the top risks of generative artificial intelligence (AI) misuse. Global adoption of generative AI necessitates red-teaming evaluations (i.e., systematic adv…
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…
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…
Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, Nicholas Pangakis +17
Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult…