在可解释的AI中绘制社会技术差距：解决XAI中差距的框架
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
摘要：
可解释人工智能（XAI）系统本质上是社会技术的;因此，它们受到社会技术差距的影响-技术可供性和社会需求之间的鸿沟。然而，绘制这一差距是具有挑战性的。在XAI的背景下，我们认为，绘制差距可以提高我们对问题的理解，这可以反射性地提供可操作的见解，以提高可解释性。利用不同领域的两个案例研究，我们从经验上推导出一个框架，通过在XAI的背景下连接AI指南并阐明如何使用它们来解决差距，从而促进系统地绘制社会技术差距。我们将该框架应用于一个新领域的第三种情况，展示其启示。最后，我们讨论了框架的概念含义，分享其操作化的实际考虑，并提供指导，将其转移到新的背景下。通过对XAI中社会技术差距的理解做出概念性和实践性的贡献，该框架扩展了XAI的设计空间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
