INFO 5390

Fairness in Machine Learning

Course Description

Course information provided by the 2025-2026 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.

Enrollment Priority Recommended prerequisite: 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 2024SP

Learning Outcomes

• Write code in Python to computationally identify fairness issues in machine learning across diverse domains and data types.
• Articulate the key tensions and trade-offs in different machine learning scenarios, and reason about their implications.
• Develop problem-solving strategies to approach fairness challenges that arise in machine learning contexts.

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