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

TL;DR

This survey reviews the evolving landscape of long-term fairness in machine learning, emphasizing challenges like feedback loops and the need for new approaches beyond static fairness measures.

Contribution

It provides a comprehensive taxonomy of long-term fairness research, highlighting key challenges and proposing future directions for addressing fairness over time.

Findings

Existing fairness methods often fail in long-term scenarios

Feedback loops can exacerbate fairness issues over time

Future research should focus on dynamic fairness approaches

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.