Demographic Parity Constrained Minimax Optimal Regression Under Linear Model

Abstract

We explore the minimax optimal error associated with a demographic parity-constrained regression problem within the context of a linear model. Our proposed model encompasses a broader range of discriminatory bias sources compared to the model presented by Chzhen and Schreuder. Our analysis reveals that the minimax optimal error for the demographic parity-constrained regression problem under our model is characterized by $\Theta(\frac{dM}{n})$, where $n$ denotes the sample size, $d$ represents the dimensionality, and $M$ signifies the number of demographic groups arising from sensitive attributes. Moreover, we demonstrate that the minimax error increases in conjunction with a larger bias present in the model.

Cite

Text

Fukuchi and Sakuma. "Demographic Parity Constrained Minimax Optimal Regression Under Linear Model." Neural Information Processing Systems, 2023.

Markdown

[Fukuchi and Sakuma. "Demographic Parity Constrained Minimax Optimal Regression Under Linear Model." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/fukuchi2023neurips-demographic/)

BibTeX

@inproceedings{fukuchi2023neurips-demographic,
  title     = {{Demographic Parity Constrained Minimax Optimal Regression Under Linear Model}},
  author    = {Fukuchi, Kazuto and Sakuma, Jun},
  booktitle = {Neural Information Processing Systems},
  year      = {2023},
  url       = {https://mlanthology.org/neurips/2023/fukuchi2023neurips-demographic/}
}