Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
It tackles the issue of long-term fairness in ML for high-stakes applications, but as a survey, it is incremental in summarizing and organizing prior work.
This survey addresses the problem of achieving fairness in machine learning systems over time, highlighting that static fairness measures are insufficient due to feedback loops and environmental interactions, and it reviews existing literature to present a taxonomy and identify key challenges.
The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications. While prior works have investigated static fairness measures, recent studies reveal that automated decision-making has long-term implications and that off-the-shelf fairness approaches may not serve the purpose of achieving long-term fairness. Additionally, the existence of feedback loops and the interaction between models and the environment introduces additional complexities that may deviate from the initial fairness goals. In this survey, we review existing literature on long-term fairness from different perspectives and present a taxonomy for long-term fairness studies. We highlight key challenges and consider future research directions, analyzing both current issues and potential further explorations.