AI 中文总结
针对自主与软件密集型系统缺乏通用可解释性定义及开发指南的问题，本文整合现有定义提出统一可解释性定义与结构化需求，为（自）可解释系统的标准开发及认证审计提供支持。
AI 中文摘要
自主系统和软件密集型系统的出现频率、复杂度及所承担的责任不断提升。由于这些系统复杂度极高，透明度和可解释性等特性必须成为研究重点。迄今为止，尚无适用于（自）可解释系统开发的通用定义与指南。欧盟人工智能法案（EU AI Act）和IEEE透明度标准7001-2021已明确认识到可解释性标准的需求。为满足这一需求，我们在分析并整合现有定义的基础上，提出了可解释性的统一定义；此外，我们还给出了构建（自）可解释系统所必需的结构化可解释性需求。通过对所得分类体系进行分析，我们建议将解释质量（即解释的正确性）纳入统一定义。我们的方法为（自）可解释系统的正式标准开发提供了支持，建立此类统一分类体系也是迈向可解释系统认证或审计的有益一步。
英文摘要
Autonomous and software-intensive systems have been growing in occurrence, complexity, and assumed responsibility. Due to the high complexity of these systems, properties like transparency and explainability must be a focus of investigation. To date, no universally applicable definition and guide for the development of (self-)explainable systems exists. A need for explainability standards has already been recognized in the EU AI Act and the IEEE Transparency Standard 7001-2021. To address this need, we propose unified definitions in explainability based on an analysis and combination of existing definitions. Additionally, we present structured explainability requirements that are necessary to build (self-)explainable systems. By analysing the resulting taxonomy, we propose the incorporation of explanation goodness and thus correctness of explanations into the unified definitions. With our approach, we support the development of formal standards for (self-)explainable systems. Establishing such a uniform taxonomy also is a beneficial step towards certifying or auditing explainable systems.
Comments10 pages, 5 figures, 2 tables
