The field of artificial intelligence (AI) is rapidly influencing health and
healthcare, but bias and poor performance persists for populations who face
widespread structural oppression. Previous work has clearly outlined the need
for more rigorous attention to data representativeness and model performance to
advance equity and reduce bias. However, there is an opportunity to also
improve the explainability of AI by leveraging best practices of social
epidemiology and health equity to help us develop hypotheses for associations
found. In this paper, we focus on explainable AI (XAI) and describe a framework
for interdisciplinary expert panel review to discuss and critically assess AI
model explanations from multiple perspectives and identify areas of bias and
directions for future research. We emphasize the importance of the
interdisciplinary expert panel to produce more accurate, equitable
interpretations which are historically and contextually informed.
Interdisciplinary panel discussions can help reduce bias, identify potential
confounders, and identify opportunities for additional research where there are
gaps in the literature. In turn, these insights can suggest opportunities for
AI model improvement.

人工智能对于健康和医疗领域产生了显著影响，但在面对受到结构性压迫的人群中仍存在偏见和性能问题。本文提出了可解释人工智能（XAI）的框架，通过跨学科专家组进行评估，旨在改进模型解释、减少偏见，并为未来研究指明方向。