Diversity dilemmas: uncovering gender and nationality biases in graduate admissions across top North American computer science programs

TL;DR

This study investigates gender and nationality biases in graduate admissions at top North American CS programs, revealing nationality bias but no gender bias, and explores how diversity relates to research success.

Contribution

It provides a comprehensive dataset and statistical analysis of gender and nationality biases in graduate admissions, highlighting the presence of nationality bias and its impact on research teams.

Findings

No gender bias found in admissions.

Nationality bias observed in admission decisions.

Diversity correlates with research achievements.

Abstract

Although different organizations have defined policies towards diversity in academia, many argue that minorities are still disadvantaged in university admissions due to biases. Extensive research has been conducted on detecting partiality patterns in the academic community. However, in the last few decades, limited research has focused on assessing gender and nationality biases in graduate admission results of universities. In this study, we collected a novel and comprehensive dataset containing information on approximately 14,000 graduate students majoring in computer science (CS) at the top 25 North American universities. We used statistical hypothesis tests to determine whether there is a preference for students' gender and nationality in the admission processes. In addition to partiality patterns, we discuss the relationship between gender/nationality diversity and the scientific achievements of research teams. Consistent with previous studies, our findings show that there is no gender bias in the admission of graduate students to research groups, but we observed bias based on students' nationality.