Post-processing fairness with minimal changes
<|reference_start|>Post-processing fairness with minimal changes: In this paper, we introduce a novel post-processing algorithm that is both model-agnostic and does not require the sensitive attribute at test time. In addition, our algorithm is explicitly designed to enforce minimal changes between biased and debiased predictions; a property that, while highly desirable, is rarely prioritized as an explicit objective in fairness literature. Our approach leverages a multiplicative factor applied to the logit value of probability scores produced by a black-box classifier. We demonstrate the efficacy of our method through empirical evaluations, comparing its performance against other four debiasing algorithms on two widely used datasets in fairness research.<|reference_end|>
@article{di gennaro2024post-processing,
    title={Post-processing fairness with minimal changes},
    author={Federico Di Gennaro, Thibault Laugel, Vincent Grari, Xavier Renard,
  Marcin Detyniecki},
    journal={arXiv preprint arXiv:2408.15096},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15096},
    primaryClass={cs.LG cs.AI}
}