Project Summary/Abstract AD/ADRD is a growing national public health crisis as the number of Americans ≥65 years is projected to double by 2050. Our parent grant was designed to measure cardiorespiratory fitness (CRF) as a biomarker of physical activity in the most extensive epidemiological study in nearly 1 million Veterans using VA’s world-class electronic health record and advanced artificial intelligence technologies. The parent grant aims are (1) to determine the relationship between CRF and incident AD/ADRD, taking into consideration a non-linear relationship and potential interactions of CRF with other risk factors and (2) to define incremental CRF levels that are linked to progressively lower risk of AD/ADRD, overall, and in subgroups by age, sex, and race. Our Aim 3 is to develop and validate a deep learning-based risk prediction model to determine the optimal CRF level for individuals to achieve the lowest risk of AD/ADRD. Deep learning is a key Artificial intelligence (AI) technique. AI has demonstrated great strides in the past decade. However, AI models are often viewed as “black box” as they are difficult to explain. Understanding what an AI model does is a prerequisite to the ethical use of AI, because stakeholders can’t trust a model or detect the potentially intended and unintended biases associated with the development or utilization of the model without understanding it. We believe that explainable AI is a powerful tool to address the bioethics issues of trust and bias. The purpose of explainable AI is to make it possible for human users to understand and trust the decisions or recommendations offered by the AI model, and to debug and refine it. Specifically, this supplement will test the effect of AI model explanation on trust and bias detection in a simulated environment by recruiting a set of stakeholders and using a scenario-based approach. The potential broad impact of the proposed work is that it will advance the ethical development and use of AI/ML in biomedical and behavioral sciences using explainable AI methods.