FairRR: Pre-Processing for Group Fairness through Randomized Response
<|reference_start|>FairRR: Pre-Processing for Group Fairness through Randomized Response: The increasing usage of machine learning models in consequential decision-making processes has spurred research into the fairness of these systems. While significant work has been done to study group fairness in the in-processing and post-processing setting, there has been little that theoretically connects these results to the pre-processing domain. This paper proposes that achieving group fairness in downstream models can be formulated as finding the optimal design matrix in which to modify a response variable in a Randomized Response framework. We show that measures of group fairness can be directly controlled for with optimal model utility, proposing a pre-processing algorithm called FairRR that yields excellent downstream model utility and fairness.<|reference_end|>
@article{zeng2024fairrr:,
    title={FairRR: Pre-Processing for Group Fairness through Randomized Response},
    author={Xianli Zeng, Joshua Ward, Guang Cheng},
    journal={arXiv preprint arXiv:2403.07780},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07780},
    primaryClass={stat.ML cs.LG}
}