Work

A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models

Year: 2024

Type: article

Abstract: Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classification (ROC) algorithm to simultaneously optimize fairness and accuracy while safeguarding protected attributes such as age and gender. Our approach seeks a multi-objective optimization solution that balances accuracy, group fairness loss, and individual fairness loss. The framework integrates fairness objectives without relying on a weighted summation method, instead focusing on directly optimizing the trade-offs. Empirical evaluations on publicly available datasets, including German Credit, Adult Income, and COMPAS, reveal several significant findings: the ROC-based approach demonstrates superior performance, achieving an accuracy of 94.29%, an individual fairness loss of 0.04, and a group fairness loss of 0.06 on the German Credit dataset. These results underscore the effectiveness of our framework, particularly the ROC component, in enhancing both the fairness and performance of machine learning models.

Source: Machine Learning and Knowledge Extraction

DOI: 10.3390/make6030105

Authors: Rashmi Nagpal, Ariba Khan, Mihir Borkar, Amar Gupta

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, San Jose State University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,046