BMFT: Achieving fairness via bias-based weight masking fine-tuning

Yuyang Xue, Junyu Yan, Raman Dutt, Fasih Haider, Jingshuai Liu, Steven McDonagh, Sotirios A. Tsaftaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationInternational Conference on Medical Image Computing and Computer-Assisted Intervention
PublisherSpringer
DOIs
Publication statusAccepted/In press - 15 Jul 2024
EventFairness of AI in Medical Imaging 2024 Workshop - Marrakesh, Morocco
Duration: 10 Oct 202410 Oct 2024
https://faimi-workshop.github.io/2024-miccai/

Workshop

WorkshopFairness of AI in Medical Imaging 2024 Workshop
Abbreviated titleFAIMI 2024
Country/TerritoryMorocco
CityMarrakesh
Period10/10/2410/10/24
Internet address

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