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Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation

arXiv 2111.05071 ↗First posted 9 Nov 2021cs.CY cs.AIcited by 19

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Abstract (arXiv)

The proposed European Artificial Intelligence Act (AIA) is the first attempt to elaborate a general legal framework for AI carried out by any major global economy. As such, the AIA is likely to become a point of reference in the larger discourse on how AI systems can (and should) be regulated. In this article, we describe and discuss the two primary enforcement mechanisms proposed in the AIA: the conformity assessments that providers of high-risk AI systems are expected to conduct, and the post-market monitoring plans that providers must establish to document the performance of high-risk AI systems throughout their lifetimes. We argue that AIA can be interpreted as a proposal to establish a Europe-wide ecosystem for conducting AI auditing, albeit in other words. Our analysis offers two main contributions. First, by describing the enforcement mechanisms included in the AIA in terminology borrowed from existing literature on AI auditing, we help providers of AI systems understand how they can prove adherence to the requirements set out in the AIA in practice. Second, by examining the AIA from an auditing perspective, we seek to provide transferable lessons from previous research about how to refine further the regulatory approach outlined in the AIA. We conclude by highlighting seven aspects of the AIA where amendments (or simply clarifications) would be helpful. These include, above all, the need to translate vague concepts into verifiable criteria and to strengthen the institutional safeguards concerning conformity assessments based on internal checks.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 15 of its 83 references that are in the map. First posted on arXiv 9 Nov 2021.

Builds on

15 of 83 references.

References · 15

  1. 2017Counterfactual Fairnesscited by 802
  2. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  3. 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 163
  4. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  5. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  6. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  7. 2018The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standardscited by 97
  8. 2017Thinking Fast and Slow: Optimization Decomposition Across Timescalescited by 60
  9. 2021Ethics-Based Auditing to Develop Trustworthy AIcited by 23
  10. 2017Responsible Autonomycited by 18
  11. 2021Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitationscited by 14
  12. 2016On the (im)possibility of fairnesscited by 11
3 more
  1. 2020Regulating Artificial Intelligence: Proposal for a Global Solutioncited by 10
  2. 2019Defining the scope of AI regulationscited by 8
  3. 2021Ethics-Based Auditing of Automated Decision-Making Systems: Intervention Points and Policy Implicationscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 7; 2025: 2; 2026: 7.

19 papers.

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  1. 2023Auditing large language models: a three-layered approachcited by 135
  2. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 60
  3. 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Reviewcited by 19
  4. 2024Auditing of AI: Legal, Ethical and Technical Approachescited by 9
  5. 2024Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil societycited by 7
  6. 2025AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliancecited by 5
  7. 2026High-Risk AI Systems and the Problem of Identity in the European AI Actcited by 4
  8. 2024Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Studycited by 3
  9. 2025Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Frameworkcited by 2
  10. 2026Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaborationcited by 2
  11. 2023Datasheets for Machine Learning Sensorscited by 1
  12. 2024The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other?cited by 1
7 more
  1. 2026The Stability Trap: Evaluating the Reliability of LLM-Based Instruction Adherence Auditingcited by 1
  2. 2026A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governancecited by 1
  3. 2024The Switch, the Ladder, and the Matrix: Models for Classifying AI Systemscited by 0
  4. 2024ARTAI: An Evaluation Platform to Assess Societal Risk of Recommender Algorithmscited by 0
  5. 2026The EU AI Act and the Rights-based Approach to Technological Governancecited by 0
  6. 2026Scaling, Lock-In, and Proxy Compliance: A Political Economy of Responsible AIcited by 0
  7. 2026An Empirical Study and Assessment of EU AI Act Compliance Checkerscited by 0