Abstract: AI, and particularly machine learning, is reshaping the way we think about scientific research and healthcare. And yet, the collection and use of patient data, its subsequent conversion into foundation and predication models, and their application in biomedical research and healthcare raises many ethical questions and societal quandaries that have the potential to thwart such activities. The goal of this presentation is to discuss how ethical reasoning and computational decision making can be embedded into the AI for health lifecycle and ultimately maximize social good. Along the way, we will review several case studies to understand how things have gone wrong in the past, but also, what can go right! This presentation will touch on issues of trust, algorithmic fairness, and data privacy.
Meet the speaker: Bradley Malin is the Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science at Vanderbilt University, as well as the Vice Chair for Research Affairs in the Department of Biomedical Informatics at Vanderbilt University Medical Center, where he co-founded and co-directs the AI Discovery and Vigilance to Accelerate Innovation and Clinical Excellence (ADVANCE) Center. His research is in the development of trustworthy artificial intelligence methodology and infrastructure. He is a principal investigator of several large consortia on artificial intelligence sponsored by the National Institutes of Health, including the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) and Bridge2AI. Among various honors, he is an elected fellow of the National Academy of Medicine (NAM), the American College of Medical Informatics (ACMI), the American Institute for Medical and Biological Engineering (AIMBE), the International Academy of Health Sciences Informatics (IAHSI), and the Institute of Electrical and Electronics Engineers (IEEE). He received his bachelor’s in biological sciences, master’s in public policy and management, and doctorate in computer science from Carnegie Mellon University.
