Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits

TL;DR

This study investigates how industry ML practitioners interact with fairness toolkits, revealing practical challenges and opportunities to improve toolkit design for better support in fairness efforts.

Contribution

It provides the first empirical analysis of real-world usage of fairness toolkits by practitioners, informing future toolkit development.

Findings

Practitioners face challenges in understanding and applying fairness tools.

There are gaps in how toolkits support communication and collaboration.

Design improvements can better scaffold responsible fairness practices.

Abstract

Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML practitioners actually use these toolkits in practice. In this paper, we conducted the first in-depth empirical exploration of how industry practitioners (try to) work with existing fairness toolkits. In particular, we conducted think-aloud interviews to understand how participants learn about and use fairness toolkits, and explored the generality of our findings through an anonymous online survey. We identified several opportunities for fairness toolkits to better address practitioner needs and scaffold them in using toolkits effectively and responsibly. Based on these findings, we highlight implications for the design of future open-source fairness toolkits that can support practitioners in better contextualizing, communicating, and collaborating around ML fairness efforts.