FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis

Raman Dutt, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy M Hospedales

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

Abstract / Description of output

Training models with robust group fairness properties is crucial in ethically sensitive application areas such as medical diagnosis. Despite the growing body of work aiming to minimise demographic bias in AI, this problem remains challenging. A key reason for this challenge is the fairness generalisation gap: High-capacity deep learning models can fit all training data nearly perfectly, and thus also exhibit perfect fairness during training. In this case, bias emerges only during testing when generalisation performance differs across subgroups. This motivates us to take a bi-level optimisation perspective on fair learning: Optimising the learning strategy based on validation fairness. Specifically, we consider the highly effective workflow of adapting pre-trained models to downstream medical imaging tasks using parameter-efficient fine-tuning (PEFT) techniques. There is a trade-off between updating more parameters, enabling a better fit to the task of interest vs. fewer parameters, potentially reducing the generalisation gap. To manage this tradeoff, we propose FairTune, a framework to optimise the choice of PEFT parameters with respect to fairness. We demonstrate empirically that FairTune leads to improved fairness on a range of medical imaging datasets
Original languageEnglish
Title of host publicationThe Twelfth International Conference on Learning Representations
Pages1-24
Number of pages24
Publication statusAccepted/In press - 16 Jan 2024
EventThe Twelfth International Conference on Learning Representations - Vienna, Austria, Vienna, Austria
Duration: 7 May 202411 May 2024
https://iclr.cc/

Conference

ConferenceThe Twelfth International Conference on Learning Representations
Country/TerritoryAustria
CityVienna
Period7/05/2411/05/24
Internet address

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