Machine Learning Ethics and Society

12 units · Graduate

Intermittent: 12 units The practice of Machine Learning (ML) increasingly involves making choices that impact real people and society at large. This course covers an array of ethical, societal, and policy considerations in applying ML tools to high-stakes domains, such as employment, education, lending, criminal justice, medicine, and beyond. We will discuss: (1) the pathways through which ML can lead to or amplify problematic decision-making practices (e.g., those exhibiting discrimination, inscrutability, invasion of privacy, and beyond); (2) recent technological methods and remedies to capture and alleviate these concerns; and (3) the scope of applicability and limitations of technological remedies in the context of several contemporary application domains. The course's primary goals are: (a) to raise awareness about the social, ethical, and policy implications of ML, and (b) to prepare students to critically analyze these issues as they emerge in the ever-expanding use of ML in socially consequential domains. Prerequisites: 10-601 or 10-715 or 10-315 or 10-301 or 10-701 Course Website: http://www.cs.cmu.edu/~hheidari/mles-spring-23.html