Towards Effective Discrimination Testing for Generative AI
Document Type
Article
Publication Date
6-2025
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 and enhance the reliability of fairness assessments in future deployments.
Disciplines
Artificial Intelligence and Robotics | Law | Science and Technology Law | Theory and Algorithms
Recommended Citation
Thomas Zollo, Nikita Rajaneesh, Richard Zemel, Talia B. Gillis & Emily Black,
Towards Effective Discrimination Testing for Generative AI,
FAccT '25: Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
1028
(2025).
Available at:
https://scholarship.law.columbia.edu/faculty_scholarship/4730