Fairness in Machine Learning

Catalog description

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.