Exploring Why Underrepresented Students Are Less Likely to Study Machine Learning and Artificial Intelligence

Abstract

There is little research on why underrepresented minorities are less likely to specifically study Machine Learning and Artificial Intelligence (ML/AI). We surveyed 159 undergraduate students about their interest in, exposure to, and personal views on ML/AI in order to explore variations in responses by self-reported gender and race/ethnicity groups. We found that students underrepresented by race/ethnicity are ∼6 times less likely to take a traditional ML/AI course than those not underrepresented by race/ethnicity, but no significant difference was found between gender representation. Additionally, students underrepresented by race/ethnicity are more likely to report interest in social, cultural, and political impacts of ML/AI rather than the more technical aspects of ML/AI itself, which is a prevalent interest of students not underrepresented by race/ethnicity. We explore potential reasoning for this difference through further analysis of their survey responses. Encouragingly, we find that regardless of representational status 72.0% of students who report lack of interest in a traditional introductory course are interested in a ML/AI course that focuses more on the political, philosophical, and ethical issues raised by ML/AI and its impacts on society. Our findings suggest that a 'CS Principles"style introductory ML/AI course, emphasizing social and political impacts, could be an effective way to promote diversity in ML/AI.