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

TL;DR

This paper examines the EU AI Act's framework for explainability, exploring approaches, challenges, and legal integration to promote transparent and accountable AI systems in critical sectors.

Contribution

It provides a comprehensive analysis of XAI techniques, implementation challenges, and the legal and policy considerations within the EU AI governance framework.

Findings

Identifies key XAI techniques relevant to EU regulations

Highlights challenges in implementing explainability in practice

Discusses the role of standard setting and enforcement in EU law

Abstract

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.