Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI

TL;DR

This paper introduces a framework for systematically identifying and addressing the sociotechnical gap in explainable AI, enhancing understanding and guiding improvements across different domains.

Contribution

It presents a novel framework that connects AI guidelines with sociotechnical considerations in XAI, validated through multiple case studies.

Findings

Framework effectively maps the sociotechnical gap in XAI

Application to new domain demonstrates framework's versatility

Provides practical guidance for XAI design improvements

Abstract

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.