CS 5550 Equity and Explainability in Machine Learning 3.0 Credits
With the rapid deployment of machine learning models in domains such as lending, sentencing, and hiring, it is essential to understand the ethical aspects and negative consequences of such models. The course focuses on two such elements: (1) ensuring the decisions made by these systems are equitable and (2) making these opaque systems more transparent and supporting users' needs for explanations. The course motivates each of these elements and provides mathematical formalization and algorithmic approaches to achieve them.
Repeat Status: Not repeatable for credit
Prerequisites: (CS 5010 [Min Grade: D] or IS 5310 [Min Grade: D] or CS 501 [Min Grade: D] or DSCI 511 [Min Grade: D]) and (CS 5030 [Min Grade: D] or IS 5320 [Min Grade: D] or CS 502 [Min Grade: D] or DSCI 501 [Min Grade: D])