MultiFair: Model Fairness With Multiple Sensitive Attributes

Huan Tian

doi:10.1109/TNNLS.2024.3384181

MultiFair: Model Fairness With Multiple Sensitive Attributes

2025-03-01

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AI

DOI：10.1109/TNNLS.2024.3384181

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While existing fairness interventions show promise in mitigating biased predictions, most studies concentrate on single-attribute protections. Although a few methods consider multiple attributes, they either require additional constraints or prediction heads, incurring high computational overhead or jeopardizing the stability of the training process. More critically, they consider per-attribute protection approaches, raising concerns about fairness gerrymandering where certain attribute combinations remain unfair. This work aims to construct a neutral domain containing fused information across all subgroups and attributes. It delivers fair predictions as the fused input contains neutralized information for all considered attributes. Specifically, we adopt mixup operations to generate samples with fused information. However, our experiments reveal that directly adopting the operations leads to degraded prediction results. The excessive mixup operations result in unrecognizable training data. To this end, we design three distinct mixup schemes that balance information fusion across attributes while retaining distinct visual features critical for training valid models. Extensive experiments with multiple datasets and up to eight sensitive attributes demonstrate that the proposed MultiFair method can deliver fairness protections for multiple attributes while maintaining valid prediction results.

Keywords:

Protection

Training

Predictive models

Measurement

Visualization

Training data

Data models

Deep learning

domain adaptation

fairness

image processing

multiple sensitive features

MultiFair: Model Fairness With Multiple Sensitive Attributes

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