Lessons from Clinical Communications for Explainable AI

Abstract

One of the major challenges in the use of opaque, complex AI models is the need or desire to provide an explanation to the end-user (and other stakeholders) as to how the system arrived at the answer it did. While there is significant research in the development of explainability techniques for AI, the question remains as to who needs an explanation, what an explanation consists of, and how to communicate this to a lay user who lacks direct expertise in the area. In this position paper, an interdisciplinary team of researchers argue that the example of clinical communications offers lessons to those interested in improving the transparency and interpretability of AI systems. We identify five lessons from clinical communications: (1) offering explanations for AI systems and disclosure of their use recognizes the dignity of those using and impacted by it; (2) AI explanations can be productively targeted rather than totally comprehensive; (3) AI explanations can be enforced through codified rules but also norms, guided by core values; (4) what constitutes a “good” AI explanation will require repeated updating due to changes in technology and social expectations; 5) AI explanations will have impacts beyond defining any one AI system, shaping and being shaped by broader perceptions of AI. We review the history, debates and consequences surrounding the institutionalization of one type of clinical communication, informed consent, in order to illustrate the challenges and opportunities that may await attempts to offer explanations of opaque AI models. We highlight takeaways and implications for computer scientists and policymakers in the context of growing concerns and moves toward AI governance.