Alzheimer's Disease (AD) is increasingly becoming an immense economic and social challenge. By 2050, it's projected that 13.8 million Americans will be living with AD, and the total annual expenditures for health, long- term care, and hospice services for those with AD and other dementias could surge to $1.2 trillion. Developing predictive artificial intelligent (AI) models to assist in the diagnosis and prognosis of the disease is critically important for patient management. However, current AI models for studying Alzheimer’s Disease often suffer from explainability challenges, and the effects of race, sex, social determinant of health (SDOH), and comorbidity are typically not considered in existing research but are critically important to consider for creating ethical AI models. Building on our team’s pioneering work in explainable AI, studying the role of race, SDOH, comorbidity in AD, and leveraging cerebrospinal fluid (CSF) proteomics for studying AD, we will develop explainable and ethical AI models for both automated disease diagnoses and prognosis. We will build AI models for multimodal data-fusion and leverage our pioneering work in CSF proteomics to understand covariation patterns between brain volumetrics and connectome, amyloid and tau depositions, and CSF proteins with a goal to enhance our understanding of AD pathophysiology. Successful completion of this study will provide improved and ethical AI models that can be further developed into effective clinical decision support tools as well as enhance our understanding of the pathophysiology of AD.