Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges

Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter Spirtes, Yang Liu, Lu Cheng

Abstract: 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.

Submission Length: Long submission (more than 12 pages of main content)

Changes Since Last Submission: As per the meta-review, we have included a new Section, "Critical Analysis and Practical Considerations," that critically dissects the literature and discusses methodological assumptions that impact and inform practical considerations. Lastly, we addressed other minor grammatical and typesetting issues

Assigned Action Editor: ~Stefan_Feuerriegel1

Submission Number: 4396