Improving fairness in machine learning systems: What do industry practitioners need?

TL;DR

This paper investigates industry practitioners' challenges in developing fairer machine learning systems through interviews and surveys, highlighting gaps between research solutions and real-world needs.

Contribution

It provides the first systematic analysis of industry needs for fairness tools in ML, informing future research directions.

Findings

Industry practitioners face specific challenges in implementing fairness.

There is a disconnect between research solutions and industry needs.

Future research should better align with real-world industry requirements.

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 industry practitioners and 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 industry practitioners' needs.