Fair, Accountable and Transparent Machine Learning

Catalog description

The bulk of the course will consist of reading and discussing papers about model fairness, model interpretability, and the ethical and accountable use of machine learning (ML), ranging from classic methods to contemporary topics. Applicable to those doing research on ML, using ML as a research tool for other fields, or thinking about the ethics and policy surrounding ML use.