A Scoresheet for Explainable AI
This work addresses the need for more concrete explainability requirements in AI systems, particularly for stakeholders in multiagent systems and other domains, though it is incremental as it builds on existing standards.
The paper tackles the gap between high-level explainability standards and practical requirements by developing a scoresheet to specify or assess explainability aspects for applications, demonstrating its generality and usefulness across various AI technologies.
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.