FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes

Empirical loss minimization during machine learning training can inadvertently introduce bias, stemming from discrimination and societal prejudices present in the data. To address the shortcomings of traditional fair machine learning methods-which often rely on sensitive information of training data or mandate significant model alterations-we present FairIF, a unique two-stage training framework. Distinctly, FairIF enhances fairness by recalibrating training sample weights using the influence function. Notably, it employs sensitive information from a validation set, rather than the training set, to determine these weights. This approach accommodates situations with missing or inaccessible sensitive training data. Our FairIF ensures fairness across demographic groups by retraining models on the reweighted data. It stands out by offering a plug-and-play solution, obviating the need for changes in model architecture or the loss function. We demonstrate that the fairness performance of FairIF is guaranteed during testing with only a minimal impact on classification performance. Additionally, we analyze that our framework adeptly addresses issues like group size disparities, distribution shifts, and class size discrepancies. Empirical evaluations on three synthetic and five real-world datasets across six model architectures confirm FairIF's efficiency and scalability. The experimental results indicate superior fairness-utility trade-offs compared to other methods, regardless of bias types or architectural variations. Moreover, the adaptability of FairIF to utilize pretrained models for subsequent tasks and its capability to rectify unfairness originating during the pretraining phase are further validated through our experiments.