CS 5382

Fairness in Machine Learning

Course Description

Course information provided by the 2026-2027 Catalog.

Machine learning is increasingly used in both high-stakes and everyday settings. Yet, its impacts are uneven, often reinforcing existing social hierarchies by disproportionately benefiting some groups while harming others. This course introduces multiple perspectives on fairness in machine learning, spanning domains in predictive and generative AI as well as tabular, image, and text data. Students will develop the skills to identify fairness challenges, understand why they are difficult to address, and reason through strategies for approaching them. Students should have experience coding in Python and have taken at least one introductory course in machine learning or data science.

Enrollment Information Recommended prerequisites: students should have experience coding in Python and have taken at least one introductory course in machine learning or data science.

Last 4 Terms Offered 2026SP, 2024SP

Learning Outcomes

• Write code in Python to computationally demonstrate biases in end-to-end algorithmic systems based on choices of data, variables, modeling, and outcomes.
• Apply mathematical definitions of fairness to real-world case studies to explain decisions made by both humans and algorithms.
• Enumerate challenges to practitioners in algorithmic-guided decision-making (including feedback loops, interpretability, and strategic behavior) and explain how these challenges can lead to broader societal impacts.

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