# Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI

arXiv: https://arxiv.org/abs/2502.14868 · HTML: https://mlsanity.com/paper/2502.14868/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 60 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Demystifying the Draft EU Artificial Intelligence Act](https://mlsanity.com/paper/2107.03721/) (2021)
- [On quantitative aspects of model interpretability](https://mlsanity.com/paper/2007.07584/) (2020)
- [Defining the scope of AI regulations](https://mlsanity.com/paper/1909.01095/) (2019)

## Built on by (most cited first, 11 of 11)

- [Robustness and Cybersecurity in the EU Artificial Intelligence Act](https://mlsanity.com/paper/2502.16184/) (2025)
- [How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review](https://mlsanity.com/paper/2503.13467/) (2025)
- [CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems](https://mlsanity.com/paper/2508.19281/) (2025)
- [The EU AI Act and the Rights-based Approach to Technological Governance](https://mlsanity.com/paper/2603.22920/) (2026)
- [When Do Data-Driven Systems Exhibit the Capability to Infer?](https://mlsanity.com/paper/2606.11769/) (2026)
- [P-TAME: Explain Any Image Classifier with Trained Perturbations](https://mlsanity.com/paper/2501.17813/) (2025)
- [AI Literacy for Legal AI Systems: A practical approach](https://mlsanity.com/paper/2505.18006/) (2025)
- [WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasets](https://mlsanity.com/paper/2506.06455/) (2025)
- [Causal Synthetic Data Generation in Recruitment](https://mlsanity.com/paper/2511.16204/) (2025)
- [Algorithmic Administration and the EU AI Act: Legal Principles for Public Sector Use of AI](https://mlsanity.com/paper/2604.22765/) (2026)
- [Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap](https://mlsanity.com/paper/2608.02699/) (2026)

## Abstract (arXiv)

The lack of explainability of Artificial Intelligence (AI) is one of the first obstacles that the industry and regulators must overcome to mitigate the risks associated with the technology. The need for eXplainable AI (XAI) is evident in fields where accountability, ethics and fairness are critical, such as healthcare, credit scoring, policing and the criminal justice system. At the EU level, the notion of explainability is one of the fundamental principles that underpin the AI Act, though the exact XAI techniques and requirements are still to be determined and tested in practice. This paper explores various approaches and techniques that promise to advance XAI, as well as the challenges of implementing the principle of explainability in AI governance and policies. Finally, the paper examines the integration of XAI into EU law, emphasising the issues of standard setting, oversight, and enforcement.