The Necessity of AI Audit Standards Boards
Not on the map. Why?
Abstract (arXiv)
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, we argue that creating auditing standards is not just insufficient, but actively harmful by proliferating unheeded and inconsistent standards, especially in light of the rapid evolution and ethical and safety challenges of AI. Instead, the paper proposes the establishment of an AI Audit Standards Board, responsible for developing and updating auditing methods and standards in line with the evolving nature of AI technologies. Such a body would ensure that auditing practices remain relevant, robust, and responsive to the rapid advancements in AI. The paper argues that such a governance structure would also be helpful for maintaining public trust in AI and for promoting a culture of safety and ethical responsibility within the AI industry. Throughout the paper, we draw parallels with other industries, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. AI auditing should emulate those fields, and extend beyond technical assessments to include ethical considerations and stakeholder engagement, but we explain that this is not enough; emulating other fields' governance mechanisms for these processes, and for audit standards creation, is a necessity. We also emphasize the importance of auditing the entire development process of AI systems, not just the final products...
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 29 of its 98 references that are in the map. First posted on arXiv 11 Apr 2024.
Builds on
29 of 98 references.
References · 29
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17 more
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- 2023Auditing large language models: a three-layered approachcited by 135
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 95
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Built on by
Citing papers per year: 2024: 1; 2025: 5; 2026: 3.
9 papers.
- 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
- 2025AI Governance through Marketscited by 5
- 2025Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliancecited by 5
- 2025A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governancecited by 5
- 2025Limits of Safe AI Deployment: Differentiating Oversight and Controlcited by 3
- 2025Balancing Safety and Helpfulness in Healthcare AI Assistants through Iterative Preference Alignmentcited by 1
- 2026Reciprocal Trust and Distrust in Artificial Intelligence Systems: The Hard Problem of Regulationcited by 0
- 2026The Two Genie Game: Adoption and Welfare in Audit-Grounded AI Governancecited by 0
- 2026AISPA: User-Centric System Prompt Auditing for Large Language Model Applicationscited by 0
- 2026AISPA: User-Centric System Prompt Auditing for Large Language Model Applications30 Jul 2026
- 2026The Two Genie Game: Adoption and Welfare in Audit-Grounded AI Governance27 Jun 2026
- 2026Reciprocal Trust and Distrust in Artificial Intelligence Systems: The Hard Problem of Regulation7 Apr 2026
- 2025Balancing Safety and Helpfulness in Healthcare AI Assistants through Iterative Preference Alignment3 Dec 2025
- 2025Limits of Safe AI Deployment: Differentiating Oversight and Control4 Jul 2025
- 2025A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance4 Feb 2025
- 2025Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance3 Feb 2025
- 2025AI Governance through Markets29 Jan 2025
- 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models1 Jun 2024