AI Ethics in Healthcare

3.0 credits

This course will provide an overview of the regulatory, ethical, and historical dimensions of doing datascience within the US healthcare system. The course will cover the philosophical and technicalconsiderations regarding ensuring fairness, privacy, consent and explainability in healthcare.One of the goals of the course is to train students to think critically about these considerationswhen choosing data science and machine learning techniques to apply to healthcare data.Topics may include: (1) the regulatory environment around clinical data capture and researchwith human subject data (2) privacy and consent to collection, (2) bias in algorithms (i.e., howcan bias occur? how should we measure unfairness in algorithms that determine the distributionof healthcare/health?), (3) “explainability” in the context of healthcare, and (4) valuesdisagreement and algorithmic medicine (e.g., how should diagnostic or prognostic algorithms bedeployed, given that we disagree about the costs and benefits of algorithmic medicine?). Eachweek will consist of detailed discussion of a philosophical work, alongside a relevant technicalcase study of algorithmic medicine.