AI, Medicine, and Society

Undergraduate

AI has shown tremendous promise to address problems in medicine and science. There is also considerable hype surrounding AI and many concerns (some justified, some not) regarding the use of this type of technology. This discussion-based seminar 1) provides undergraduate students across disciplines with a broad overview of issues related to AI in medicine at a non-technical level, drawing on perspectives from the interpretive/humanistic social sciences, computing, engineering, and healthcare and 2) models interdisciplinary communication and build a robust framework for collaboration. Overarching topics, grounded in medical case studies, include what it means for computers to “think” and how we understand what they are thinking about; the use and limits of scientific knowledge in making policy decisions; bias, fairness, equity, equality; the challenges of implementation of AI systems; safety and risk; and the human/computer interface. The course also provides a high level overview of machine learning, discussing opportunities, limitations, and tradeoffs. Ultimately, the course offers a grounded look at how AI is being discussed and deployed on the ground in medicine, equipping students with a critical lens for thinking about responsible and practical implementation and innovation when it comes to AI. SO TTh 2:35pm-3:50pm