mlsanityData to 14 Oct 2026

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

arXiv 2502.14868 ↗First posted 24 Jan 2025cs.CY cs.AIcited by 11

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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

  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  2. 2021Demystifying the Draft EU Artificial Intelligence Actcited by 84
  3. 2020On quantitative aspects of model interpretabilitycited by 71
  4. 2019Defining the scope of AI regulationscited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 4.

11 papers.