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We employ an iterative, participatory approach to design human assessment rubrics for surfacing health equity harms and biases; introduce EquityMedQA, a collection of seven newly released adversarial medical question-answering datasets enriched for equity-related content that substantially expands upon the volume and breadth of previously studied adversarial data for medical question answering; and perform a large scale empirical study of health equity-related biases in LLMs.

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