Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?
This work addresses the problem of aligning technical XAI methods with regulatory transparency requirements for policymakers and AI developers, though it is incremental as it builds on existing discussions without introducing new methods or data.
The paper identifies a 'transparency gap' between explainable AI (XAI), which focuses narrowly on explaining algorithmic properties, and the EU AI Act, which views transparency as supporting broader values like accountability and human rights, and proposes four practical axes to bridge this gap.
The European Union has proposed the Artificial Intelligence Act which introduces detailed requirements of transparency for AI systems. Many of these requirements can be addressed by the field of explainable AI (XAI), however, there is a fundamental difference between XAI and the Act regarding what transparency is. The Act views transparency as a means that supports wider values, such as accountability, human rights, and sustainable innovation. In contrast, XAI views transparency narrowly as an end in itself, focusing on explaining complex algorithmic properties without considering the socio-technical context. We call this difference the ``transparency gap''. Failing to address the transparency gap, XAI risks leaving a range of transparency issues unaddressed. To begin to bridge this gap, we overview and clarify the terminology of how XAI and European regulation -- the Act and the related General Data Protection Regulation (GDPR) -- view basic definitions of transparency. By comparing the disparate views of XAI and regulation, we arrive at four axes where practical work could bridge the transparency gap: defining the scope of transparency, clarifying the legal status of XAI, addressing issues with conformity assessment, and building explainability for datasets.