Machine Learning and Artificial Intelligence are powering the applications we use, the decisions we make, and the decisions made about us. We have already seen numerous examples of what happens when these algorithms are designed without diversity in mind: facial recognition algorithms, recidivism algorithms, and resume reviewing algorithms all produce non-equitable outcomes. As Machine Learning (ML) and Artificial Intelligence (AI) expand into more areas of our lives, we must take action to promote diversity among those working in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field. In this talk, I will outline the findings from two iterations of survey-based studies that start to build a model of intentional persistence in the field. I will highlight the findings that suggest drivers of the gender gap, review what we’ve learned about persistence through these studies, and share open areas for future work.

Sharon Ferguson

Industrial Engineering

University of Toronto