Human and Algorithmic Bias

Course Number: 47954

The purpose of this course is to help students develop an economic perspective on algorithmic bias and related social and policy issues. To this end, we will read and discuss (1) classic economics, psychology and sociology papers on human bias and discrimination, (2) recent papers in the field of computer science/machine learning about the algorithmic bias and fairness, and (3) papers that examine algorithmic bias through an economics lens, with (3) being the focus of this course. By the end of this course, students are expected to have a good understanding of the existing literature on human and algorithmic bias, and be able to develop research ideas around algorithmic bias and articulate their research ideas to an academic audience.

Degree: PhD
Concentration: Business Technologies
Academic Year: 2023-2024
Semester(s): Mini 4
Required/Elective: Elective
Units: 6

Format

Lecture: 100min/wk and Recitation: 50min/wk

Textbook(s):

Research papers (available on Canvas)

Learning Objectives

After completing this course, students are expected to gain a good understanding of the existing literature on human and algorithmic bias, and be able to develop research ideas related to algorithmic bias, and more broadly, the economics of machine learning.