Achieving Fairness at No Utility Cost via Data Reweighing with Influence
This addresses fairness in algorithmic governance for applications like hiring or lending, offering a method that avoids the typical utility trade-off, though it is incremental as it builds on existing reweighing techniques.
The paper tackles the problem of achieving fairness in machine learning models without sacrificing predictive utility by proposing a data reweighing method that assigns individual weights to training samples based on their influence on fairness and utility, empirically demonstrating cost-free fairness for equal opportunity on real-world datasets.
With the fast development of algorithmic governance, fairness has become a compulsory property for machine learning models to suppress unintentional discrimination. In this paper, we focus on the pre-processing aspect for achieving fairness, and propose a data reweighing approach that only adjusts the weight for samples in the training phase. Different from most previous reweighing methods which usually assign a uniform weight for each (sub)group, we granularly model the influence of each training sample with regard to fairness-related quantity and predictive utility, and compute individual weights based on influence under the constraints from both fairness and utility. Experimental results reveal that previous methods achieve fairness at a non-negligible cost of utility, while as a significant advantage, our approach can empirically release the tradeoff and obtain cost-free fairness for equal opportunity. We demonstrate the cost-free fairness through vanilla classifiers and standard training processes, compared to baseline methods on multiple real-world tabular datasets. Code available at https://github.com/brandeis-machine-learning/influence-fairness.