Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI
This addresses the need for explainable AI to mitigate risks in critical domains like healthcare and justice, but it is incremental as it reviews existing approaches and regulatory frameworks without introducing new methods.
The paper examines the EU Artificial Intelligence Act's approach to explainability in AI, exploring techniques to advance XAI and challenges in implementing explainability principles in governance and policies, with a focus on integration into EU law including standard setting and enforcement.
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.