3 citations · 10 across the 12 of their papers we have counts for
12 papers
ASSERT: A Measurement Pipeline for GenAI Audits
Riccardo Fogliato, Abhinav Palia, Xiawei Wang +11
Audits of generative AI (GenAI) systems often summarize behavior as a reported rate: how often the audited system complies with policy. Researchers and stakeholders use that rate t…
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…
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)…
Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems
Emma Harvey, Emily Sheng, Su Lin Blodgett +4
The NLP research community has made publicly available numerous instruments for measuring representational harms caused by large language model (LLM)-based systems. These instrumen…
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…
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 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…
Gaps Between Research and Practice When Measuring Representational Harms Caused by LLM-Based Systems
Emma Harvey, Emily Sheng, Su Lin Blodgett +4
To facilitate the measurement of representational harms caused by large language model (LLM)-based systems, the NLP research community has produced and made publicly available nume…
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…
"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviors
Li Lucy, Su Lin Blodgett, Milad Shokouhi +2
Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to behave identically for social groups, to ad…
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging
Jared Katzman, Angelina Wang, Morgan Scheuerman +4
In this paper, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct categories, each with its own…
Measuring Representational Harms in Image Captioning
Angelina Wang, Solon Barocas, Kristen Laird +1
Previous work has largely considered the fairness of image captioning systems through the underspecified lens of "bias." In contrast, we present a set of techniques for measuring f…