Improving Fairness in Machine Learning Systems

TLDR

The potential for ML systems to amplify social inequities and unfairness is receiving increasing attention, prompting recent work on algorithmic tools to assess and mitigate such unfairness. The study aims to ensure these tools positively impact industry practice by grounding their design in real‑world needs. The authors conducted 35 semi‑structured interviews and an anonymous survey of 267 ML practitioners to systematically investigate commercial product teams' challenges and needs for supporting fairer ML systems. They found misalignments between practitioners' challenges and existing fair‑ML research solutions, and propose research directions to better address these needs.

Abstract

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by teams in practice and the solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address practitioners' needs.