Hanna Wallach - Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?

The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent research has focused on the development of algorithmic tools to detect and mitigate such unfairness. However, if these tools are to have a positive impact on industry practice, it is crucial that their design be informed by an understanding of industry teams’ actual needs. Through semi-structured interviews with 35 machine learning practitioners, spanning 19 teams and 10 companies, and an anonymous survey of 267 practitioners, we conducted the first systematic investigation of industry teams' challenges and needs for support in developing fairer machine learning systems. I will describe this work and summarize areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the academic literature. Based on these findings, I will highlight directions for future research that will better address practitioners' needs.