COS 534: Fairness in Machine Learning
Machine learning discovers and reproduces patterns in existing data. Thus, unthinking application of ML risks perpetuating societal biases including racial and gender bias. This course is about the emerging science of fairness in ML. Topics include: sources of bias in ML; methods for detecting, measuring, and mitigating bias; comparison of fairness criteria; data modeling versus algorithmic modeling; causal inference and fairness; other topics in AI ethics including privacy, accountability, transparency, power, and justice. Students take on hands-on empirical projects of their choosing.
Semester:
Spring21
Lectures:
Tuesday,Thursday, 11:00-12:20
Location:
TBD
Faculty
Arvind Narayanan
Office:
Sherrerd Hall 308
Extension:
9302
Email:
arvindn
Additional Information
The Graduate Coordinator is Nicki Mahler
Email:
ngotsis
Office:
Computer Science 213
Extension:
5387