Algorithmic Auditing under Data Access Mandates: A risk limiting framework for third party evaluations of AI fairness

Abstract

As AI systems become more prominent in decision-making for domains such as employment and advertising, ensuring fairness in these models is increasingly important. In this work, we design a black-box, risk-limiting audit framework for assessing fairness with the four-fifths rule. Inspired by election auditing techniques and sequential hypothesis testing, we propose two-group and multi-group algorithms that maintain risk-limiting guarantees and stop early for innocent models. Unlike fixed-sample methods, our approach evaluates fairness while continuously sampling, allowing auditors to repeatedly request more data when needed. We demonstrate the effectiveness of our algorithms through empirical evaluations on real-world employment datasets collected for New York City's Local Law 144. The audits detect fairness violations correctly 100% of the time and verify fairness after sampling on average 66% of the data in the multi-group setting. Our approach enables third-party auditors to efficiently and confidently evaluate fairness claims, even in settings with limited transparency.