research-article

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Black-Box Access is Insufficient for Rigorous AI Audits

FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency

Pages 2254 - 2272

https://doi.org/10.1145/3630106.3659037

Published: 05 June 2024 Publication History

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Abstract

External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depends on the degree of access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on black-box access, in which auditors can only query the system and observe its outputs. However, white-box access to the system’s inner workings (e.g., weights, activations, gradients) allows an auditor to perform stronger attacks, more thoroughly interpret models, and conduct fine-tuning. Meanwhile, outside-the-box access to training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows auditors to scrutinize the development process and design more targeted evaluations. In this paper, we examine the limitations of black-box audits and the advantages of white- and outside-the-box audits. We also discuss technical, physical, and legal safeguards for performing these audits with minimal security risks. Given that different forms of access can lead to very different levels of evaluation, we conclude that (1) transparency regarding the access and methods used by auditors is necessary to properly interpret audit results, and (2) white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.

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Cited By

Cen SAlur R(2024)From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI AuditingProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694711(1-14)Online publication date: 29-Oct-2024
https://doi.org/10.1145/3689904.3694711
Nannini LMarchiori Manerba MBeretta I(2024)Mapping the landscape of ethical considerations in explainable AI researchEthics and Information Technology10.1007/s10676-024-09773-726:3Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1007/s10676-024-09773-7
Nguyen TSaghir ATran KNguyen DLuong NTran K(2024)Safety and Reliability of Artificial Intelligence SystemsArtificial Intelligence for Safety and Reliability Engineering10.1007/978-3-031-71495-5_9(185-199)Online publication date: 29-Sep-2024
https://doi.org/10.1007/978-3-031-71495-5_9

Index Terms

Black-Box Access is Insufficient for Rigorous AI Audits
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy
2. Social and professional topics
  1. Computing / technology policy
    1. Government technology policy
      1. Governmental regulations

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FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency

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DOI:10.1145/3630106

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Cen SAlur R(2024)From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI AuditingProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694711(1-14)Online publication date: 29-Oct-2024
https://doi.org/10.1145/3689904.3694711
Nannini LMarchiori Manerba MBeretta I(2024)Mapping the landscape of ethical considerations in explainable AI researchEthics and Information Technology10.1007/s10676-024-09773-726:3Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1007/s10676-024-09773-7
Nguyen TSaghir ATran KNguyen DLuong NTran K(2024)Safety and Reliability of Artificial Intelligence SystemsArtificial Intelligence for Safety and Reliability Engineering10.1007/978-3-031-71495-5_9(185-199)Online publication date: 29-Sep-2024
https://doi.org/10.1007/978-3-031-71495-5_9

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