Fair Regression: Quantitative Definitions and Reduction-based Algorithms

05/30/2019
by   Alekh Agarwal, et al.
0

In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems fair regression. We propose general schemes for fair regression under two notions of fairness: (1) statistical parity, which asks that the prediction be statistically independent of the protected attribute, and (2) bounded group loss, which asks that the prediction error restricted to any protected group remain below some pre-determined level. While we only study these two notions of fairness, our schemes are applicable to arbitrary Lipschitz-continuous losses, and so they encompass least-squares regression, logistic regression, quantile regression, and many other tasks. Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions. In addition to analyzing theoretical properties of our schemes, we empirically demonstrate their ability to uncover fairness--accuracy frontiers on several standard datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/19/2019

Fair quantile regression

Quantile regression is a tool for learning conditional distributions. In...
research
10/05/2022

Conformalized Fairness via Quantile Regression

Algorithmic fairness has received increased attention in socially sensit...
research
06/16/2021

Costs and Benefits of Wasserstein Fair Regression

Real-world applications of machine learning tools in high-stakes domains...
research
02/16/2021

Lexicographically Fair Learning: Algorithms and Generalization

We extend the notion of minimax fairness in supervised learning problems...
research
06/12/2019

Pairwise Fairness for Ranking and Regression

We present pairwise metrics of fairness for ranking and regression model...
research
06/23/2021

Fairness for Image Generation with Uncertain Sensitive Attributes

This work tackles the issue of fairness in the context of generative pro...
research
01/29/2019

General Fair Empirical Risk Minimization

We tackle the problem of algorithmic fairness, where the goal is to avoi...

Please sign up or login with your details

Forgot password? Click here to reset