Achieving Equity via Transfer Learning With Fairness Optimization

Xiaoyang Wang

doi:10.1109/ACCESS.2024.3519465

2024-01-01

Xiaoyang Wang

Chia‐Hsuan Chang

Christopher C. Yang *

DOI：10.1109/ACCESS.2024.3519465

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摘要

En 中文

Machine learning algorithms are increasingly used in real-world decision-making systems, raising concerns about potential biases and unfairness. Existing in-processing bias mitigation approaches often focus on achieving numerical parity across demographic groups while neglecting the performance impact on individual groups and the overall performance-fairness trade-off. This can lead to increased discriminatory outcomes and hinder efforts to develop truly fair AI systems. This paper proposes Transfer Learning with Fairness Optimization (TLFO), a novel framework that serially optimizes predictive performance and fairness in machine learning models. TLFO leverages transfer learning by dividing the training process into two distinct phases: (1) initial learning for performance optimization and (2) subsequent fine-tuning for fairness enhancement. This sequential approach enables fine-grained control over fairness constraints, minimizing the performance-fairness trade-off. Extensive experiments on two real-world datasets demonstrate TLFO's effectiveness. TLFO consistently achieves superior fairness with minimal performance degradation compared to state-of-the-art in-processing bias mitigation approaches, highlighting its potential for generating fair and accurate classifiers with versatile applications.

Keyword:

Optimization

Transfer learning

Training

Predictive models

Machine learning algorithms

Prevention and mitigation

Adaptation models

Data models

Accuracy

Transforms

AI fairness