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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 language | English |
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Title of host publication | International Conference on Medical Image Computing and Computer-Assisted Intervention |
Publisher | Springer |
DOIs | |
Publication status | Accepted/In press - 15 Jul 2024 |
Event | Fairness of AI in Medical Imaging 2024 Workshop - Marrakesh, Morocco Duration: 10 Oct 2024 → 10 Oct 2024 https://faimi-workshop.github.io/2024-miccai/ |
Workshop
Workshop | Fairness of AI in Medical Imaging 2024 Workshop |
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Abbreviated title | FAIMI 2024 |
Country/Territory | Morocco |
City | Marrakesh |
Period | 10/10/24 → 10/10/24 |
Internet address |
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- 2 Finished
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From trivial representations to learning concepts in AI by exploiting unique data
1/02/23 → 31/01/25
Project: Research
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