P2NIA: Privacy-Preserving Non-Iterative Auditing

2025-04-01

AI Summary

Ethical auditing of high-risk AI systems faces two critical challenges: (1) privacy leakage risks arising from auditors’ reliance on platform APIs, and (2) biased fairness assessments due to auditors’ inability to access raw training or inference data. Method: We propose a non-iterative, privacy-preserving collaborative auditing paradigm that eliminates both real-data sharing and repeated API queries. Our end-to-end protocol integrates differentially private synthetic data generation, local model training, and statistical inference. Contribution/Results: Experiments demonstrate significant reductions in platform API maintenance overhead and privacy risk; fairness metric estimation bias decreases by over 40%, while accuracy substantially improves. To our knowledge, this is the first framework enabling mutually beneficial collaboration between platforms and auditors—offering a scalable, verifiable, and privacy-safe auditing pathway compliant with emerging AI governance standards.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Privacy & Security
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Audits of web technologies, standards, platforms, and applications

Abstract

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.

Problem

• Ensures ethical compliance of high-risk AI systems
• Reduces privacy risks in traditional API-based audits
• Minimizes estimation bias without data distribution knowledge

Innovation

• Privacy-preserving non-iterative auditing scheme
• Uses synthetic or local data
• Avoids traditional API-based audits