BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning
<|reference_start|>BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning: Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness in machine learning require a substantial amount of training data and depend on model retraining, which may not be practical in real-world scenarios. To mitigate these challenges, we propose Bias-based Weight Masking Fine-Tuning (BMFT), a novel post-processing method that enhances the fairness of a trained model in significantly fewer epochs without requiring access to the original training data. BMFT produces a mask over model parameters, which efficiently identifies the weights contributing the most towards biased predictions. Furthermore, we propose a two-step debiasing strategy, wherein the feature extractor undergoes initial fine-tuning on the identified bias-influenced weights, succeeded by a fine-tuning phase on a reinitialised classification layer to uphold discriminative performance. Extensive experiments across four dermatological datasets and two sensitive attributes demonstrate that BMFT outperforms existing state-of-the-art (SOTA) techniques in both diagnostic accuracy and fairness metrics. Our findings underscore the efficacy and robustness of BMFT in advancing fairness across various out-of-distribution (OOD) settings. Our code is available at: https://github.com/vios-s/BMFT<|reference_end|>
@article{xue2024bmft:,
    title={BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning},
    author={Yuyang Xue, Junyu Yan, Raman Dutt, Fasih Haider, Jingshuai Liu, Steven
  McDonagh, and Sotirios A. Tsaftaris},
    journal={arXiv preprint arXiv:2408.06890},
    year={2024},
    doi={10.1007/978-3-031-72787-0_10},
    archivePrefix={arXiv},
    eprint={2408.06890},
    primaryClass={cs.LG cs.AI}
}