Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
This work addresses the problem of aligning XAI systems with human needs for researchers and practitioners, though it is incremental as it builds on existing sociotechnical concepts.
The paper tackles the challenge of the sociotechnical gap in Explainable AI (XAI) by developing a framework to systematically chart and address the divide between technical affordances and social needs, using case studies to demonstrate its application and expand the XAI design space.
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.