Regression under demographic parity constraints via unlabeled post-processing
<|reference_start|>Regression under demographic parity constraints via unlabeled post-processing: We address the problem of performing regression while ensuring demographic parity, even without access to sensitive attributes during inference. We present a general-purpose post-processing algorithm that, using accurate estimates of the regression function and a sensitive attribute predictor, generates predictions that meet the demographic parity constraint. Our method involves discretization and stochastic minimization of a smooth convex function. It is suitable for online post-processing and multi-class classification tasks only involving unlabeled data for the post-processing. Unlike prior methods, our approach is fully theory-driven. We require precise control over the gradient norm of the convex function, and thus, we rely on more advanced techniques than standard stochastic gradient descent. Our algorithm is backed by finite-sample analysis and post-processing bounds, with experimental results validating our theoretical findings.<|reference_end|>
@article{chzhen2024regression,
    title={Regression under demographic parity constraints via unlabeled
  post-processing},
    author={Evgenii Chzhen (LMO, CELESTE), Mohamed Hebiri (LAMA), Gayane Taturyan
  (LAMA, IMT)},
    journal={arXiv preprint arXiv:2407.15453},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15453},
    primaryClass={stat.ML cs.CY cs.LG}
}