Fairness in Machine Learning
University of Pennsylvania, Spring 2025
Instructor: Nikita Bezrukov
Time: TR 10:15am–11:45am
Place: Bennett 222
This course explores the theme of fairness in machine learning, focusing on how algorithmic systems can perpetuate or mitigate societal biases. It delves into the ethical, technical, and social implications of these systems, examining key concepts such as individual and group fairness, bias detection, and the trade-offs involved in achieving equitable outcomes. In parallel, the course emphasizes academic writing, guiding students in effectively communicating complex ideas, constructing rigorous arguments, and engaging critically with scholarly literature. The course aims to develop both a deep understanding of fairness in ML and strong academic writing skills.
Syllabus: Tah-dam.
Tentative schedule
January 15
Read:
Chapter 1
Chapter 2
Submit:
Pset 1
PSet 2
Topics:
Introduction
Conclusion
January 25
Read:
Chapter 1
Chapter 2
Submit:
Pset 1
PSet 2
Topics:
Introduction
Conclusion