Explainable and reproducible AI: culturally responsive AI for health equity in minoritized groups.

Artificial intelligence (AI) is transforming healthcare by enabling advanced diagnostics, personalized treatments, and improved operational efficiencies. By identifying complex data patterns and correlations, AI could supplement clinical decision-making, enabling more rapid diagnoses and treatment decisions tailored to meet the unique needs of diverse communities. However, realizing these benefits requires that clinical AI models be consistent, reliable, and validated across diverse populations and clinical environments. In addition, as these data patterns and correlations may often be unexpected, AI models require more explainability compared to other medical technologies. This is especially true for complex models, where the processes driving a model to make a prediction are often unclear and uninterpretable to both model developers and medical professionals, resulting in AI models frequently being described as "black boxes". To address this fundamental challenge of interpretability, explainable AI (XAI) has emerged as a critical approach, providing insight (often in a