Exploring Fairness Interventions in Open Source Projects
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
The deployment of biased machine learning (ML) models has resulted in adverse
effects in crucial sectors such as criminal justice and healthcare. To address
these challenges, a diverse range of machine learning fairness interventions
have been developed, aiming to mitigate bias and promote the creation of more
equitable models. Despite the growing availability of these interventions,
their adoption in real-world applications remains limited, with many
practitioners unaware of their existence. To address this gap, we
systematically identified and compiled a dataset of 62 open source fairness
interventions and identified active ones. We conducted an in-depth analysis of
their specifications and features to uncover considerations that may drive
practitioner preference and to identify the software interventions actively
maintained in the open source ecosystem. Our findings indicate that 32% of
these interventions have been actively maintained within the past year, and 50%
of them offer both bias detection and mitigation capabilities, mostly during
inprocessing.
