Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI
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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.
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
4 of 60 references.
References · 4
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
- 2021Demystifying the Draft EU Artificial Intelligence Actcited by 84
- 2020On quantitative aspects of model interpretabilitycited by 71
- 2019Defining the scope of AI regulationscited by 8
Built on by
Citing papers per year: 2025: 7; 2026: 4.
11 papers.
- 2025Robustness and Cybersecurity in the EU Artificial Intelligence Actcited by 11
- 2025How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Reviewcited by 2
- 2025CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systemscited by 1
- 2026The EU AI Act and the Rights-based Approach to Technological Governancecited by 1
- 2026When Do Data-Driven Systems Exhibit the Capability to Infer?cited by 1
- 2025P-TAME: Explain Any Image Classifier with Trained Perturbationscited by 0
- 2025AI Literacy for Legal AI Systems: A practical approachcited by 0
- 2025WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasetscited by 0
- 2025Causal Synthetic Data Generation in Recruitmentcited by 0
- 2026Algorithmic Administration and the EU AI Act: Legal Principles for Public Sector Use of AIcited by 0
- 2026Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gapcited by 0
- 2026Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap3 Aug 2026
- 2026When Do Data-Driven Systems Exhibit the Capability to Infer?10 Jun 2026
- 2026Algorithmic Administration and the EU AI Act: Legal Principles for Public Sector Use of AI24 Mar 2026
- 2026The EU AI Act and the Rights-based Approach to Technological Governance24 Mar 2026
- 2025Causal Synthetic Data Generation in Recruitment20 Nov 2025
- 2025CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems24 Aug 2025
- 2025WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasets6 Jun 2025
- 2025AI Literacy for Legal AI Systems: A practical approach23 May 2025
- 2025How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review28 Feb 2025
- 2025Robustness and Cybersecurity in the EU Artificial Intelligence Act22 Feb 2025
- 2025P-TAME: Explain Any Image Classifier with Trained Perturbations29 Jan 2025