Trustworthy Machine Learning
Explore ways in which contemporary machine learning (ML) algorithms can be untrustworthy, along with algorithmic approaches to mitigate these trust issues. Discuss algorithmic fairness, privacy in ML, ML security, and interpretability/explicability in ML. Examine fairness, security, and privacy issues in ML. Only one of the following may be counted: Computer Science 365T, 378 (Topic: Trustworthy Machine Learning), 378 (Topic: Safe and Ethical AI). Three lecture hours a week for one semester.
Prerequisites & course information
Explore ways in which contemporary machine learning (ML) algorithms can be untrustworthy, along with algorithmic approaches to mitigate these trust issues. Discuss algorithmic fairness, privacy in ML, ML security, and interpretability/explicability in ML. Examine fairness, security, and privacy issues in ML. Only one of the following may be counted: Computer Science 365T, 378 (Topic: Trustworthy Machine Learning), 378 (Topic: Safe and Ethical AI). Three lecture hours a week for one semester.
Sources
- Course catalogue ↗2026-2027