Interdisciplinary expertise to advance human-centered explainable AI

Abstract

The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and inadequate subgroup performance persists. Previous work has clearly outlined the need for more rigorous attention to data representativeness and model performance to advance population health and reduce bias. However, there is an opportunity to leverage best practices of social epidemiology, behavior science, and health services research to help develop hypotheses for associations found, particularly when using explainable AI (XAI) methods. This paper introduces a novel human-in-the-loop model that couples generative XAI with a wraparound interdisciplinary expert panel review process. This human-centered approach critically assesses AI model explanations from multiple perspectives, effectively identifying areas of bias and pinpointing directions for future research. We summarize the approach, highlighting processes and findings where the interdisciplinary expert panel produced interpretations which were historically and contextually informed. In particular, the interdisciplinary panel identified potential confounders and brought in scientific evidence from previous studies, which have clear implications to reduce bias. This promising human-centered framework not only maximizes multidisciplinary methodological rigor for AI model improvement and eventual implementation but also proactively fosters team science opportunities in future research.

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