Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917931670044672 |
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| author | Pavlidis, Georgios |
| author_facet | Pavlidis, Georgios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI Pavlidis, Georgios Computers and Society Artificial Intelligence 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. |
| title | Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2502.14868 |