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Law and the Emerging Political Economy of Algorithmic Audits

Terzis, Petros, Veale, Michael, Gaumann, Noëlle·2024·The 2024 ACM Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3630106.3658970

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  • The lack of access and visibility to many AI systems leaves auditors without the information needed to make adequate and truly independent assessments.

    “leaves auditors without the information needed to make adequate and truly independent assessments [70, 75].”

    From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [75]

    Section 5.0.2 (Confidentiality; page unavailable) says rigorous algorithmic audits require access to documents that organisations may withhold, and that independence challenges may make auditors less forceful in seeking them. This supports the concern, but the paper presents restricted access as a risk rather than establishing that auditors generally lack the information needed for adequate, independent assessments of many AI systems. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Terzis, P., Veale, M., & Gaumann, N.
Affiliations
  • Institute for Information Law, University of Amsterdam, Netherlands and Faculty of Laws, University College London, United Kingdom
  • Faculty of Laws, University College London, United Kingdom and Institute for Information Law, University of Amsterdam, Netherlands
  • Faculty of Laws, University College London, United Kingdom
ORCID
Venue
The 2024 ACM Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2024-06-03
Pages
1255-1267
DOI
10.1145/3630106.3658970
Cited by
21
References
10
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
UK Research and Innovation (EP/V00784X/1)

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