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.

翻译：欧盟提出了《人工智能法案》，其中对AI系统的透明度提出了详细要求。这些要求中的许多内容可以通过可解释人工智能（XAI）领域来满足，然而，XAI与该法案在透明度定义上存在根本性差异。该法案将透明度视为支撑更广泛价值观的手段，例如问责制、人权和可持续创新。相比之下，XAI狭义地将透明度视为目的本身，专注于解释复杂的算法特性，而不考虑社会技术背景。我们将这一差异称为"透明度鸿沟"。若未能弥合透明度鸿沟，XAI可能面临一系列透明度问题无法得到解决的风险。为开始弥合这一鸿沟，我们梳理并澄清了XAI与欧洲法规——即该法案及相关的《通用数据保护条例》（GDPR）——在透明度基本定义上的术语差异。通过比较XAI与法规的不同观点，我们归纳出四个可通过实际工作弥合透明度鸿沟的维度：界定透明度范围、明确XAI的法律地位、解决符合性评估问题以及构建数据集的可解释性。