Balancing Fairness and Accuracy in Data-Restricted Binary Classification
<|reference_start|>Balancing Fairness and Accuracy in Data-Restricted Binary Classification: Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier. For example, in some applications, a classifier may not have direct access to sensitive attributes, affecting its ability to produce accurate and fair decisions. This paper proposes a framework that models the trade-off between accuracy and fairness under four practical scenarios that dictate the type of data available for analysis. Prior works examine this trade-off by analyzing the outputs of a scoring function that has been trained to implicitly learn the underlying distribution of the feature vector, class label, and sensitive attribute of a dataset. In contrast, our framework directly analyzes the behavior of the optimal Bayesian classifier on this underlying distribution by constructing a discrete approximation it from the dataset itself. This approach enables us to formulate multiple convex optimization problems, which allow us to answer the question: How is the accuracy of a Bayesian classifier affected in different data restricting scenarios when constrained to be fair? Analysis is performed on a set of fairness definitions that include group and individual fairness. Experiments on three datasets demonstrate the utility of the proposed framework as a tool for quantifying the trade-offs among different fairness notions and their distributional dependencies.<|reference_end|>
@article{lazri2024balancing,
    title={Balancing Fairness and Accuracy in Data-Restricted Binary Classification},
    author={Zachary McBride Lazri, Danial Dervovic, Antigoni Polychroniadou, Ivan
  Brugere, Dana Dachman-Soled, and Min Wu},
    journal={arXiv preprint arXiv:2403.07724},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07724},
    primaryClass={cs.LG cs.AI cs.CY stat.ML}
}