Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Gohar, Usman, Tang, Zeyu, Wang, Jialu, Zhang, Kun, Spirtes, Peter L., Liu, Yang, Cheng, Lu
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
Awards: Interpretable graphical models for large multi-modal COPD data (R01HL159805), Collaborative Research: III: Medium: Knowledge discovery from highly heterogeneous, sparse and private data in biomedical informatics, SCH: Fair and Interpretable Prediction of Alzheimer's Disease and MCI using LLMs, G6671297155, AI Institute for Societal Decision Making (AI-SDM), CAREER: Conformal Methods for Responsible Language Models
OpenAlex ID: W4399597608