FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes
This addresses fairness issues in machine learning for practical applications, offering a method that is less invasive and data-intensive than existing approaches, though it is incremental in building on influence functions.
The paper tackles the problem of fairness in deep learning by proposing FAIRIF, a two-stage training algorithm that reweights data to balance performance across demographic groups, requiring only a small validation set with sensitive attributes. Experiments show it improves fairness-utility trade-offs on synthetic and real-world datasets, and alleviates unfairness in pretrained models without performance loss.
Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their practical application. To address this issue, we propose a two-stage training algorithm named FAIRIF. It minimizes the loss over the reweighted data set (second stage) where the sample weights are computed to balance the model performance across different demographic groups (first stage). FAIRIF can be applied on a wide range of models trained by stochastic gradient descent without changing the model, while only requiring group annotations on a small validation set to compute sample weights. Theoretically, we show that, in the classification setting, three notions of disparity among different groups can be mitigated by training with the weights. Experiments on synthetic data sets demonstrate that FAIRIF yields models with better fairness-utility trade-offs against various types of bias; and on real-world data sets, we show the effectiveness and scalability of FAIRIF. Moreover, as evidenced by the experiments with pretrained models, FAIRIF is able to alleviate the unfairness issue of pretrained models without hurting their performance.