'I Know You Are Discriminatory!': Automated Substantiating for Individual Fairness Auditing of AI Systems

Abstract

Artificial intelligence (AI) systems are playing an increasingly crucial role in people's lives, and their frequent unfair behaviors raise concerns about fairness. To unveil the unfairness in AI systems, researchers conduct fairness auditing on these systems. However, existing fairness auditing works often focus on group fairness while ignoring discriminatory phenomena among individuals. To unearth discriminatory phenomena against individuals within AI systems, this paper proposes an individual fairness auditing framework, termed ''substantiating'', which can identify discrimination instances within AI systems by constructing individual samples. To construct these samples for substantiating, auditors often have to rely on subjective prior knowledge, lacking guidelines on how to construct unfair samples. To address this issue, this paper introduces two categories of automated sample generation methods that can rapidly find unfair samples within a limited number of queries to the system and demonstrate their effectiveness through experiments. This paper evaluates the proposed auditing framework among three categories of stakeholders in AI fairness: auditors, AI model developers, and non-technical personnel. The research findings point out their demand for individual fairness audits of AI systems and highlight how the framework supports a reliable and convenient individual fairness audit.