Towards Effective Discrimination Testing for Generative AI
Thomas P. Zollo, Nikita Rajaneesh, Richard Zemel, Talia B. Gillis,, Emily Black
TL;DR
This paper highlights the gap between current bias assessment methods and regulatory fairness goals in Generative AI, demonstrating how misalignment can lead to discriminatory outcomes and proposing practical improvements.
Contribution
It identifies misalignments between legal and technical approaches to bias evaluation and offers recommendations to improve discrimination testing for better regulatory compliance.
Findings
Misalignment causes discriminatory outcomes in real-world deployments
Case studies illustrate failures of current fairness testing methods
Recommendations aim to align bias assessment with regulatory goals
Abstract
Generative AI (GenAI) models present new challenges in regulating against discriminatory behavior. In this paper, we argue that GenAI fairness research still has not met these challenges; instead, a significant gap remains between existing bias assessment methods and regulatory goals. This leads to ineffective regulation that can allow deployment of reportedly fair, yet actually discriminatory, GenAI systems. Towards remedying this problem, we connect the legal and technical literature around GenAI bias evaluation and identify areas of misalignment. Through four case studies, we demonstrate how this misalignment between fairness testing techniques and regulatory goals can result in discriminatory outcomes in real-world deployments, especially in adaptive or complex environments. We offer practical recommendations for improving discrimination testing to better align with regulatory goals…
Peer Reviews
Decision·Submitted to ICLR 2025
This paper is grammatically well-written, and provide coverage of important recent regulatory discussion motivating the work. The authors touch on a number of important questions. The discussions, specifically around red-teaming and single vs multi turn interaction, were interesting, though I am less familiar with that body of literature on red-teaming and so rely on other reviewers to discuss its relevance and novelty. The paper is timely and thus of interest to ICLR's audience.
I have a number of concerns with this work that lead me to believe that it is not ready for publication. I will speak to the broadest concern first, then focus on specifics of experimental protocol second. Paper structure. This paper is quite broad in its application. The case studies are relatively shallow, as a result, and connecting the themes together weakens the contribution. For example, I believe that the experiments and discussion regarding red teaming and the single vs multi-turn are c
The paper discusses a timely an important topic, and some of the case studies are quite compelling (in particular the ones from 4.2 and 4.4). As far as I can tell, the main originality of the paper lies in the connection to the legal literature, and the case studies showcasing certain important phenomena, which make the assessment of the fairness of models difficult or legally challenging. I think the paper is quite clear, and may be somewhat significant for an audience that is not particularl
Despite the fact that the phenomena being showcased are important, I don't think most of them are novel: I expect that most practitioners would not find any of the case studies surprising, and would already know most of the phenomena being discussed. Let's take for instance the first case study: the authors make the case that using quality metrics like ROGUE will not necessarily correlate with fairness. I don't think this would surprise anyone: this is exactly what spurred many evaluations that
I enjoyed reading this paper - it is clear and well written. Further, it provides a good narrative of existing literature and collates four major challenges in assessing discrimination of generative AI. The experiments are sound, well thought out, and give evidence for the issues identified. Thus, the motivation for the provided recommendations on how best to assess discrimination are clearly motivated. This is a highly topical and important field which needs urgent consideration. Mapping legal
Although the recommendations are well supported, they are general, not very actionable and are thus not a great addition to the paper. The abstract claims the recommendations are 'practical' which I think is a bit misleading. For example, in the "Mitigation" part of Section 4.1 (line 295 onwards) the following recommendations are made: - "fairness researchers should attempt to create metrics and testing regimes that shed light on how GenAI behavior may influence decision-makers' perceptions of c
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Taxonomy
TopicsMachine Learning and Data Classification · Explainable Artificial Intelligence (XAI) · Natural Language Processing Techniques
MethodsALIGN