Towards Effective Discrimination Testing for Generative AI

2024-12-30

AI Summary

Current fairness testing for generative AI exhibits a fundamental misalignment with legal and regulatory objectives, enabling systems that appear formally compliant yet perpetuate substantive discrimination to pass regulatory scrutiny. Method: This paper introduces the first systematic framework bridging legal requirements and technical assessment—termed “Regulation-Aligned Discrimination Testing.” It integrates legal text analysis, counterfactual reasoning, scenario-based stress testing, and adaptive environmental modeling to yield an interpretable, auditable, and regulator-ready discrimination detection pipeline. Contribution/Results: We identify four canonical misalignment patterns and propose actionable test enhancements. Empirical evaluation demonstrates that our framework significantly improves detection rates for real-world discriminatory behaviors and strengthens regulatory trustworthiness, effectively closing the critical gap between GenAI fairness verification and compliance enforcement.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Generative AI
• Fairness Testing
• Regulatory Challenges

Innovation

• Generative AI
• Fairness Testing
• Regulatory Alignment