Towards Effective Discrimination Testing for Generative AI
Keywords: Generative AI, LLMs, fairness, discrimination, policy, diffusion
Abstract: Generative AI (GenAI) models present new challenges in testing for, and regulating against, discriminatory behavior. In this paper, we argue that GenAI fairness research still has not met these challenges: there is a dearth of reliable bias assessment methods for GenAI systems that speak to 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.
Supplementary Material: zip
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 12192
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