10-613 Machine Learning Ethics and Society

12 units, Carnegie Mellon University

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 highstakes 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 decisionmaking 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 courses 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 everexpanding use of ML in socially consequential domains.

Prerequisites: 10-301 or 10-315 or 10-701 or 10-601 or 10-715

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