# Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation arXiv: https://arxiv.org/abs/2111.05071 ยท HTML: https://mlsanity.com/paper/2111.05071/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 15 of its 83 references that are in the map. - First posted on arXiv 9 Nov 2021. ## Builds on (most cited first, 15 of 15) - [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017) - [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020) - [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020) - [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018) - [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019) - [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019) - [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018) - [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017) - [Ethics-Based Auditing to Develop Trustworthy AI](https://mlsanity.com/paper/2105.00002/) (2021) - [Responsible Autonomy](https://mlsanity.com/paper/1706.02513/) (2017) - [Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations](https://mlsanity.com/paper/2110.10980/) (2021) - [On the (im)possibility of fairness](https://mlsanity.com/paper/1609.07236/) (2016) - [Regulating Artificial Intelligence: Proposal for a Global Solution](https://mlsanity.com/paper/2005.11072/) (2020) - [Defining the scope of AI regulations](https://mlsanity.com/paper/1909.01095/) (2019) - [Ethics-Based Auditing of Automated Decision-Making Systems: Intervention Points and Policy Implications](https://mlsanity.com/paper/2111.04380/) (2021) ## Built on by (most cited first, 19 of 19) - [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023) - [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023) - [Mapping the Ethics of Generative AI: A Comprehensive Scoping Review](https://mlsanity.com/paper/2402.08323/) (2024) - [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024) - [Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society](https://mlsanity.com/paper/2403.07904/) (2024) - [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025) - [High-Risk AI Systems and the Problem of Identity in the European AI Act](https://mlsanity.com/paper/2605.23922/) (2026) - [Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Study](https://mlsanity.com/paper/2411.08906/) (2024) - [Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework](https://mlsanity.com/paper/2501.08046/) (2025) - [Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration](https://mlsanity.com/paper/2604.21554/) (2026) - [Datasheets for Machine Learning Sensors](https://mlsanity.com/paper/2306.08848/) (2023) - [The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other?](https://mlsanity.com/paper/2407.06234/) (2024) - [The Stability Trap: Evaluating the Reliability of LLM-Based Instruction Adherence Auditing](https://mlsanity.com/paper/2601.11783/) (2026) - [The EU AI Act and the Rights-based Approach to Technological Governance](https://mlsanity.com/paper/2603.22920/) (2026) - [A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance](https://mlsanity.com/paper/2607.16130/) (2026) - [The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems](https://mlsanity.com/paper/2407.05341/) (2024) - [ARTAI: An Evaluation Platform to Assess Societal Risk of Recommender Algorithms](https://mlsanity.com/paper/2409.12396/) (2024) - [Scaling, Lock-In, and Proxy Compliance: A Political Economy of Responsible AI](https://mlsanity.com/paper/2607.28023/) (2026) - [An Empirical Study and Assessment of EU AI Act Compliance Checkers](https://mlsanity.com/paper/2609.36228/) (2026) ## 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.