This report first takes stock of XAI-related requirements appearing in various EU directives, regulations, guidelines, and CJEU case law. This analysis of existing requirements will permit us to have a clearer vision of the purposes, the ``why'', of XAI, which we separate into five categories: contestability, empowerment/redressing information asymmetries, control over system performance, evaluation of algorithmic decisions, and public administration transparency. The analysis of legal requirements also permits us to create four categories of recipients for explainability: data science teams; human operators of the system; persons affected by algorithmic decisions, and regulators/judges/auditors. Lastly, we identify four main operational contexts for explainability: XAI for the upstream design and testing phase; XAI for human-on-the-loop control; XAI for human-in-the-loop control; and XAI for ex-post challenges and investigations.Second, we will present user-centered design methodology, which takes the purposes, the recipients and the operational context into account in order to develop optimal XAI solutions.Third, we will suggest a methodology to permit suppliers and users of high-risk AI applications to propose local XAI solutions that are effective in the sense of being ``meaningful'', for example, useful in light of the operational, safety and fundamental rights contexts. The process used to develop these ``meaningful'' XAI solutions will be based on user-centric design principles examined in the second part.Fourth, we will suggest that the European Commission issue guidelines to provide a harmonised approach to defining ``meaningful'' explanations based on the purposes, audiences and operational contexts of AI systems. These guidelines would apply to the AI Act, but also to the other EU texts requiring explanations for algorithmic systems and results.

翻译：本报告首先梳理了欧盟各项指令、法规、指南及欧洲法院判例中出现的与可解释人工智能（XAI）相关的要求。对这些现有要求的分析，有助于我们更清晰地认识XAI的宗旨及“为何”需要XAI，我们将其划分为五类：可争辩性、赋权/纠正信息不对称、对系统性能的控制、算法决策的评估以及公共行政透明度。法律要求的分析还使我们能够为可解释性创建四类接收者：数据科学团队；系统的人类操作员；受算法决策影响的个人；以及监管者/法官/审计员。最后，我们确定了可解释性的四种主要操作情境：用于上游设计和测试阶段的XAI；用于人环外控制的XAI；用于人环内控制的XAI；以及用于事后质疑和调查的XAI。其次，我们将介绍用户中心设计方法论，该方法考虑宗旨、接收者和操作情境，以开发最优的XAI解决方案。第三，我们将提出一种方法论，使高风险AI应用的供应商和用户能够提出局部XAI解决方案，这些方案在“有意义”的意义上是有效的，例如，在操作、安全及基本权利背景下切实有用。开发这些“有意义”XAI解决方案的过程将基于第二部分所考察的用户中心设计原则。第四，我们将建议欧盟委员会发布指南，以提供基于AI系统的宗旨、受众和操作情境来定义“有意义”解释的统一方法。这些指南将适用于《人工智能法案》，也适用于其他要求解释算法系统和结果的欧盟文本。