为AI可解释性设计评分表，帮助评估系统透明度。
A Scoresheet for Explainable AI
- 基于多利益相关方需求构建可解释性评分框架
- 提供可操作的评估标准，适用于多智能体系统等场景
- 适合关注AI透明度与信任建立的研究者和开发者
可解释性对自主智能系统的透明度至关重要，有助于建立适当的信任。尽管已有大量研究提出解释方法，并存在一些透明度标准，但现有标准过于抽象，未能充分定义可解释性要求。本文提出一个评分表，可用于明确可解释性需求或评估特定应用中的可解释性表现。该评分表综合考虑了多方利益相关者的诉求，适用于多智能体系统及其他AI技术。文中还提供了使用指南，并通过多个应用场景展示了其通用性与实用性。
原文摘要 · Abstract (English)
Explainability is important for the transparency of autonomous and intelligent systems and for helping to support the development of appropriate levels of trust. There has been considerable work on developing approaches for explaining systems and there are standards that specify requirements for transparency. However, there is a gap: the standards are too high-level and do not adequately specify requirements for explainability. This paper develops a scoresheet that can be used to specify explainability requirements or to assess the explainability aspects provided for particular applications. The scoresheet is developed by considering the requirements of a range of stakeholders and is applicable to Multiagent Systems as well as other AI technologies. We also provide guidance for how to use the scoresheet and illustrate its generality and usefulness by applying it to a range of applications.
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