Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligence.

Machine Learning (ML) and Artificial Intelligence (AI) are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms to recidivism algorithms, when they are designed without diversity in mind. Thus, we must take action to promote diversity among those in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field. While the persistence of diverse populations has been studied in engineering, there is a lack of research investigating factors that influence persistence in ML/AI. In this work, we present the advancement of a model of intentional persistence in ML/AI by surveying students in ML/AI courses. We examine persistence across demographic groups, such as gender, international student status, student loan status, and visible minority status. We investigate independent variables that distinguish ML/AI from other STEM fields, such as the varying emphasis on non-technical skills, the ambiguous ethical implications of the work, and the highly competitive and lucrative nature of the field. Our findings suggest that short-term intentional persistence is associated with academic enrollment factors such as major and level of study. Long-term intentional persistence is correlated with measures of professional role confidence. Unique to our study, we show that wanting your work to have a positive social benefit is a negative predictor of long-term intentional persistence, and women generally care more about this. We provide recommendations to educators to meaningfully discuss ML/AI ethics in classes and encourage the development of interpersonal skills to help increase diversity in the field.