Public vs Private Bodies: Who Should Run Advanced AI Evaluations and Audits? A Three-Step Logic Based on Case Studies of High-Risk Industries

• URL: http://arxiv.org/abs/2407.20847v2
• Date: Tue, 3 Sep 2024 18:11:53 GMT
• Title: Public vs Private Bodies: Who Should Run Advanced AI Evaluations and Audits? A Three-Step Logic Based on Case Studies of High-Risk Industries
• Authors: Merlin Stein, Milan Gandhi, Theresa Kriecherbauer, Amin Oueslati, Robert Trager,
• Abstract summary: This paper draws from nine such regimes to inform who should audit which parts of advanced AI. The effective responsibility distribution between public and private auditors depends heavily on specific industry and audit conditions. Public bodies' capacity should scale with the industry's risk level, size and market concentration.
• Score: 0.5573180584719433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) Safety Institutes and governments worldwide are deciding whether they evaluate and audit advanced AI themselves, support a private auditor ecosystem or do both. Auditing regimes have been established in a wide range of industry contexts to monitor and evaluate firms' compliance with regulation. Auditing is a necessary governance tool to understand and manage the risks of a technology. This paper draws from nine such regimes to inform (i) who should audit which parts of advanced AI; and (ii) how much capacity public bodies may need to audit advanced AI effectively. First, the effective responsibility distribution between public and private auditors depends heavily on specific industry and audit conditions. On the basis of advanced AI's risk profile, the sensitivity of information involved in the auditing process, and the high costs of verifying safety and benefit claims of AI Labs, we recommend that public bodies become directly involved in safety critical, especially gray- and white-box, AI model evaluations. Governance and security audits, which are well-established in other industry contexts, as well as black-box model evaluations, may be more efficiently provided by a private market of evaluators and auditors under public oversight. Secondly, to effectively fulfill their role in advanced AI audits, public bodies need extensive access to models and facilities. Public bodies' capacity should scale with the industry's risk level, size and market concentration, potentially requiring 100s of employees for auditing in large jurisdictions like the EU or US, like in nuclear safety and life sciences.

Related papers

• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing [1.196505602609637]
  Audits can take many forms, including pre-deployment risk assessments, ongoing monitoring, and compliance testing. There are many operational challenges to AI auditing that complicate its implementation. We argue that auditing can be cast as a natural hypothesis test, draw parallels hypothesis testing and legal procedure, and argue that this framing provides clear and interpretable guidance on audit implementation.
  arXiv  Detail & Related papers  (2024-10-07T06:15:46Z)
• Open Problems in Technical AI Governance [93.89102632003996]
  Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
  arXiv  Detail & Related papers  (2024-07-20T21:13:56Z)
• Auditing of AI: Legal, Ethical and Technical Approaches [0.0]
  AI auditing is a rapidly growing field of research and practice. Different approaches to AI auditing have different affordances and constraints. The next step in the evolution of auditing as an AI governance mechanism should be the interlinking of these available approaches.
  arXiv  Detail & Related papers  (2024-07-07T12:49:58Z)
• The Necessity of AI Audit Standards Boards [0.0]
  We argue that creating auditing standards is not just insufficient, but actively harmful by proliferating unheeded and inconsistent standards. Instead, the paper proposes the establishment of an AI Audit Standards Board, responsible for developing and updating auditing methods and standards.
  arXiv  Detail & Related papers  (2024-04-11T15:08:24Z)
• A Safe Harbor for AI Evaluation and Red Teaming [124.89885800509505]
  Some researchers fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. We propose that major AI developers commit to providing a legal and technical safe harbor. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
  arXiv  Detail & Related papers  (2024-03-07T20:55:08Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem [0.971392598996499]
  We provide the first comprehensive field scan of the AI audit ecosystem. We identify emerging best practices as well as methods and tools that are becoming commonplace. We outline policy recommendations to improve the quality and impact of these audits.
  arXiv  Detail & Related papers  (2023-10-04T01:40:03Z)
• Audit and Assurance of AI Algorithms: A framework to ensure ethical algorithmic practices in Artificial Intelligence [0.0]
  U.S. lacks strict legislative prohibitions or specified protocols for measuring damages. From autonomous vehicles and banking to medical care, housing, and legal decisions, there will soon be enormous amounts of algorithms. Governments, businesses, and society would have an algorithm audit, which would have systematic verification that algorithms are lawful, ethical, and secure.
  arXiv  Detail & Related papers  (2021-07-14T15:16:40Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.