Investigating Algorithmic Decision-Making with Applications to Student Diversity at Illinois Tech

Abstract

This study focuses on algorithmic decision-making and how structural inequalities can introduce inadvertent bias in an algorithm. There are a lot of efforts by universities in the United States to diversify the student body. Most of these efforts focus on outreach, but few focus on improving any internal processes first.

This study aimed to look for room for improvement to make internal processes more equitable so the rest of the efforts to diversify a student body would be more effective. Specifically, Illinois Tech admissions data was observed and patterns found were tested for statistical significance.