Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the sociotechnical gap--divide between the technical affordances and the social needs. However, charting this gap is challenging. In the context of XAI, we argue that charting the gap improves our problem understanding, which can reflexively provide actionable insights to improve explainability. Utilizing two case studies in distinct domains, we empirically derive a framework that facilitates systematic charting of the sociotechnical gap by connecting AI guidelines in the context of XAI and elucidating how to use them to address the gap. We apply the framework to a third case in a new domain, showcasing its affordances. Finally, we discuss conceptual implications of the framework, share practical considerations in its operationalization, and offer guidance on transferring it to new contexts. By making conceptual and practical contributions to understanding the sociotechnical gap in XAI, the framework expands the XAI design space.

翻译：可解释人工智能系统本质上是社会技术系统；因此，它们不可避免地面临社会技术鸿沟——即技术能力与社会需求之间的脱节。然而，如何系统性地描绘这一鸿沟仍颇具挑战。在XAI语境下，本文认为通过描绘这一鸿沟可以深化问题认知，进而反思性地为提升可解释性提供可操作洞察。我们基于两个不同领域的案例研究，实证推导出一个分析框架，该框架通过关联XAI语境下的人工智能准则，并阐明如何运用这些准则弥合鸿沟，从而促进对社会技术鸿沟的系统性描绘。我们将该框架应用于新领域的第三个案例，展示了其应用价值。最后，我们探讨了该框架的概念意涵，分享了实施过程中的实践考量，并提供了将其迁移至新场景的指导原则。该框架通过概念与实践双重层面的贡献深化了对XAI中社会技术鸿沟的理解，拓展了XAI的设计空间。