P2NIA: Privacy-Preserving Non-Iterative Auditing
This work addresses the challenge of privacy-preserving auditing for AI systems under emerging legislation, though it appears incremental as it builds on existing auditing frameworks.
The paper tackles the problem of auditing high-risk AI systems for ethical compliance by addressing the burden on platforms and estimation bias for auditors in traditional API-based methods, resulting in a novel scheme called P2NIA that enhances fairness assessments using synthetic or local data.
The emergence of AI legislation has increased the need to assess the ethical compliance of high-risk AI systems. Traditional auditing methods rely on platforms' application programming interfaces (APIs), where responses to queries are examined through the lens of fairness requirements. However, such approaches put a significant burden on platforms, as they are forced to maintain APIs while ensuring privacy, facing the possibility of data leaks. This lack of proper collaboration between the two parties, in turn, causes a significant challenge to the auditor, who is subject to estimation bias as they are unaware of the data distribution of the platform. To address these two issues, we present P2NIA, a novel auditing scheme that proposes a mutually beneficial collaboration for both the auditor and the platform. Extensive experiments demonstrate P2NIA's effectiveness in addressing both issues. In summary, our work introduces a privacy-preserving and non-iterative audit scheme that enhances fairness assessments using synthetic or local data, avoiding the challenges associated with traditional API-based audits.