The necessity of AI audit standards boards

Abstract Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing artificial intelligence (AI) systems have therefore understandably gained momentum. However, current approaches are not just insufficient, but can be actively harmful. Transparency alone does not address concerns about risk. Internal auditing is insufficient, and easily becomes safety-washing. External audit is better, but requires credible standards. Industry-led approaches to building standards or to perform audits lack credibility and undermine other efforts. Regulation often is ill adapted and becomes a static barrier. Lastly, all of these limited technical, governance, and even ethical assessments fail to ensure continued stakeholder input and engagement. Instead, the paper proposes the establishment of an AI Audit Standards Board, in line with best practices in other fields, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. This would address the evolving nature of AI technologies, help maintain public trust in AI, and promote a culture of safety and ethical responsibility within the AI industry. By ensuring audits remain relevant, robust, and responsive to the rapid advancements in AI, auditing AI will not devolve into safety washing and addresses risks and ethical concerns that will continue to arise as AI becomes increasingly important in society, and as human interaction with these systems changes over time.

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Publication Details

Journal
AI & Society
Published
2025-05-05
DOI
https://doi.org/10.1007/s00146-025-02320-y
Citations
14
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
33.12

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article

The necessity of AI audit standards boards

David Manheim, Mikhail Samin, Ross Greutzmacher, Sammy Martin et al.
14 citations
AI & Society
Ethics and Social Impacts of AI
33.12
article

The necessity of AI audit standards boards

David Manheim, Mikhail Samin, Ross Greutzmacher, Sammy Martin, Mark Bailey
article en
14 citations

Abstract

Abstract Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing artificial intelligence (AI) systems have therefore understandably gained momentum. However, current approaches are not just insufficient, but can be actively harmful. Transparency alone does not address concerns about risk. Internal auditing is insufficient, and easily becomes safety-washing. External audit is better, but requires credible standards. Industry-led approaches to building standards or to perform audits lack credibility and undermine other efforts. Regulation often is ill adapted and becomes a static barrier. Lastly, all of these limited technical, governance, and even ethical assessments fail to ensure continued stakeholder input and engagement. Instead, the paper proposes the establishment of an AI Audit Standards Board, in line with best practices in other fields, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. This would address the evolving nature of AI technologies, help maintain public trust in AI, and promote a culture of safety and ethical responsibility within the AI industry. By ensuring audits remain relevant, robust, and responsive to the rapid advancements in AI, auditing AI will not devolve into safety washing and addresses risks and ethical concerns that will continue to arise as AI becomes increasingly important in society, and as human interaction with these systems changes over time.

AI & SocietyVol. 40(8)
Institute on Governance (CA), Technion – Israel Institute of Technology (IL), Alternative Futures Associates (US), National Intelligence University (US)
Technion-Israel Institute of Technology
Openalex Percentile: Top 0%
Ethics and Social Impacts of AI
33.12
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