# Demystifying the Draft EU Artificial Intelligence Act

arXiv: https://arxiv.org/abs/2107.03721 · HTML: https://mlsanity.com/paper/2107.03721/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 74 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (2018)
- [ShaRF: Shape-conditioned Radiance Fields from a Single View](https://mlsanity.com/paper/2102.08860/) (2021)
- [X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers](https://mlsanity.com/paper/2009.11278/) (2020)
- ["What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness](https://mlsanity.com/paper/2011.02282/) (2020)
- [Algorithms that Remember: Model Inversion Attacks and Data Protection Law](https://mlsanity.com/paper/1807.04644/) (2018)
- [Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"?](https://mlsanity.com/paper/1803.07540/) (2018)
- [Consent Management Platforms under the GDPR: processors and/or controllers?](https://mlsanity.com/paper/2104.06861/) (2021)

## Built on by (most cited first, 30 of 84)

- [Regulating ChatGPT and other Large Generative AI Models](https://mlsanity.com/paper/2302.02337/) (2023)
- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts](https://mlsanity.com/paper/2201.10295/) (2022)
- [Understanding accountability in algorithmic supply chains](https://mlsanity.com/paper/2304.14749/) (2023)
- [Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models](https://mlsanity.com/paper/2407.13934/) (2024)
- [What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring](https://mlsanity.com/paper/2303.11341/) (2023)
- [A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication](https://mlsanity.com/paper/2111.04424/) (2021)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report](https://mlsanity.com/paper/2210.15767/) (2022)
- [The Conflict Between Explainable and Accountable Decision-Making Algorithms](https://mlsanity.com/paper/2205.05306/) (2022)
- [The European AI Liability Directives -- Critique of a Half-Hearted Approach and Lessons for the Future](https://mlsanity.com/paper/2211.13960/) (2022)
- [Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability](https://mlsanity.com/paper/2304.14864/) (2023)
- [Duwak: Dual Watermarks in Large Language Models](https://mlsanity.com/paper/2403.13000/) (2024)
- [Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI](https://mlsanity.com/paper/2502.14868/) (2025)
- [Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?](https://mlsanity.com/paper/2302.10766/) (2023)
- [AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act](https://mlsanity.com/paper/2406.18211/) (2024)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery](https://mlsanity.com/paper/2404.10356/) (2024)
- [Trustworthy AI: Securing Sensitive Data in Large Language Models](https://mlsanity.com/paper/2409.18222/) (2024)
- [The Risks of Machine Learning Systems](https://mlsanity.com/paper/2204.09852/) (2022)
- [Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm](https://mlsanity.com/paper/2304.02176/) (2023)
- [Integrating Generative Artificial Intelligence in Intelligent Vehicle Systems](https://mlsanity.com/paper/2305.17137/) (2023)
- [Law and the Emerging Political Economy of Algorithmic Audits](https://mlsanity.com/paper/2406.11855/) (2024)
- [SARC: A Governance-by-Architecture Framework for Agentic AI Systems](https://mlsanity.com/paper/2605.07728/) (2026)
- [On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services](https://mlsanity.com/paper/2111.01306/) (2021)
- [SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection](https://mlsanity.com/paper/2302.08618/) (2023)
- [EvalCards: A Framework for Standardized Evaluation Reporting](https://mlsanity.com/paper/2511.21695/) (2025)
- [Bias and Fairness in Computer Vision Applications of the Criminal Justice System](https://mlsanity.com/paper/2208.03209/) (2022)
- [Closed-Loop View of the Regulation of AI: Equal Impact across Repeated Interactions](https://mlsanity.com/paper/2209.01410/) (2022)
- [What constitutes a Deep Fake? The blurry line between legitimate processing and manipulation under the EU AI Act](https://mlsanity.com/paper/2412.09961/) (2024)

## Abstract (arXiv)

In April 2021, the European Commission proposed a Regulation on Artificial Intelligence, known as the AI Act. We present an overview of the Act and analyse its implications, drawing on scholarship ranging from the study of contemporary AI practices to the structure of EU product safety regimes over the last four decades. Aspects of the AI Act, such as different rules for different risk-levels of AI, make sense. But we also find that some provisions of the Draft AI Act have surprising legal implications, whilst others may be largely ineffective at achieving their stated goals. Several overarching aspects, including the enforcement regime and the risks of maximum harmonisation pre-empting legitimate national AI policy, engender significant concern. These issues should be addressed as a priority in the legislative process.