PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge

Authors

• Bojian Hou
• Andrés Mondragón
• Davoud Ataee Tarzanagh
• Zhuoping Zhou
• Andrew J Saykin
• Jason H Moore
• Marylyn D Ritchie
• Qi Long
• Li Shen

Language

English

Publication Date

1-1-2024

Journal

AMIA Summits on Translational Science Proceedings

PMID

38827072

PMCID

PMC11141835

PubMedCentral® Posted Date

5-31-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Fairness is crucial in machine learning to prevent bias based on sensitive attributes in classifier predictions. However, the pursuit of strict fairness often sacrifices accuracy, particularly when significant prevalence disparities exist among groups, making classifiers less practical. For example, Alzheimer's disease (AD) is more prevalent in women than men, making equal treatment inequitable for females. Accounting for prevalence ratios among groups is essential for fair decision-making. In this paper, we introduce prior knowledge for fairness, which incorporates prevalence ratio information into the fairness constraint within the Empirical Risk Minimization (ERM) framework. We develop the Prior-knowledge-guided Fair ERM (PFERM) framework, aiming to minimize expected risk within a specified function class while adhering to a prior-knowledge-guided fairness constraint. This approach strikes a flexible balance between accuracy and fairness. Empirical results confirm its effectiveness in preserving fairness without compromising accuracy.

Published Open-Access

yes

Recommended Citation

Hou, Bojian; Mondragón, Andrés; Tarzanagh, Davoud Ataee; et al., "PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge" (2024). Faculty, Staff and Student Publications. 922.
https://digitalcommons.library.tmc.edu/uthshis_docs/922