What Makes a Fairness Tool Project Sustainable in Open Source?

TL;DR

This paper investigates the sustainability of open-source fairness tools in AI, analyzing 50 projects to understand community engagement and maintenance challenges, highlighting the need for more stable and enduring fairness solutions.

Contribution

It provides a systematic analysis of the sustainability and maintenance of open-source fairness tools, identifying factors influencing their long-term viability.

Findings

53% of fairness projects become inactive within three years

Diverse community engagement levels observed across projects

Significant variation in maintenance practices and sustainability

Abstract

As society becomes increasingly reliant on artificial intelligence, the need to mitigate risk and harm is paramount. In response, researchers and practitioners have developed tools to detect and reduce undesired bias, commonly referred to as fairness tools. Many of these tools are publicly available for free use and adaptation. While the growing availability of such tools is promising, little is known about the broader landscape beyond well-known examples like AI Fairness 360 and Fairlearn. Because fairness is an ongoing concern, these tools must be built for long-term sustainability. Using an existing set of fairness tools as a reference, we systematically searched GitHub and identified 50 related projects. We then analyzed various aspects of their repositories to assess community engagement and the extent of ongoing maintenance. Our findings show diverse forms of engagement with these tools, suggesting strong support for open-source development. However, we also found significant variation in how well these tools are maintained. Notably, 53 percent of fairness projects become inactive within the first three years. By examining sustainability in fairness tooling, we aim to promote more stability and growth in this critical area.