A Scoresheet for Explainable AI
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 48 references that are in the map. First posted on arXiv 14 Feb 2025.
Builds on
6 of 48 references.
References · 6
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
- 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
- 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
- 2022The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligencecited by 26
- 2021More Similar Values, More Trust? -- the Effect of Value Similarity on Trust in Human-Agent Interactioncited by 7
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Mechanism Plausibility in Generative Agent-Based Modeling12 May 2026