  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.
