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The European Union has proposed the Artificial Intelligence Act whichintroduces detailed requirements of transparency for AI systems. Many of theserequirements can be addressed by the field of explainable AI (XAI), however,there is a fundamental difference between XAI and the Act regarding whattransparency is. The Act views transparency as a means that supports widervalues, such as accountability, human rights, and sustainable innovation. Incontrast, XAI views transparency narrowly as an end in itself, focusing onexplaining complex algorithmic properties without considering thesocio-technical context. We call this difference the ``transparency gap''.Failing to address the transparency gap, XAI risks leaving a range oftransparency issues unaddressed. To begin to bridge this gap, we overview andclarify the terminology of how XAI and European regulation -- the Act and therelated General Data Protection Regulation (GDPR) -- view basic definitions oftransparency. By comparing the disparate views of XAI and regulation, we arriveat four axes where practical work could bridge the transparency gap: definingthe scope of transparency, clarifying the legal status of XAI, addressingissues with conformity assessment, and building explainability for datasets.

Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?