Machine Learning, Ethics, and Society

Instructor: Hoda Heidari
Teaching Assistant: Chirag Gupta
Office hours: TBA and by appointment
Lectures: Tuesdays and Thursdays, 10:10AM--11:30AM, Posner Hall 152.

Course Description

Overview. 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. Topics that will be covered include:

Prerequisite knowledge. Background in Machine Learning is a prerequisite. In particular, students are expected to have adequate knowledge of probability, statistics, linear algebra, and optimization as it pertains to standard ML topics. Background in algorithm design, mechanism design and game theory, and coding skills will be helpful but not necessary.

Performance Assessment. The final grade will be computed based on:

For a more detailed syllabus, please view this document. (Note: you need to be signed in via your CMU credentials to be able to access the document).

Important Announcements and Statements

Please visit to course syllabus for statements regarding wellness, diversity, disability accommodations, and academic integrity. In the same document, you can find course policies regarding classroom expectations, attendance and participation, recording, and late/make-up work.

We will be using Canvas for future announcements, assignments, and publishing of the course material. This page will not be updated frequently.