Fair Regression: Quantitative Definitions and Reduction-based Algorithms

2019-05-30 · Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu

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 \emph{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.

📄 PDF Abstract BibTeX arXiv:1905.12843

Code (4)

CogComp/iclr_database

Microsoft/fairlearn

fairlearn/fairlearn

steven7woo/fair_regression_reduction

Tasks

AttributeFairness Predictionquantile regressionregression

A Reductions Approach to Fair Classification

2018-03-06 · ICML 2018 7 · Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford 외

We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied de…

Binary Classification Classification Fairness General Classification

50 Years of Test (Un)fairness: Lessons for Machine Learning

2018-11-25 · Ben Hutchinson, Margaret Mitchell

Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness h…

BIG-bench Machine Learning Fairness

Lexicographically Fair Learning: Algorithms and Generalization

2021-02-16 · Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns 외

We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short). Informally, given a collection of demographic groups of inte…

Fairness Generalization Bounds

Review of Mathematical frameworks for Fairness in Machine Learning

2020-05-26 · Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes

A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented from a mathematical point of view. Following our independence-based approach, we consi…

BIG-bench Machine Learning Fairness regression

Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness

2022-09-20 · Ian Davidson, S. S. Ravi

Existing work on fairness typically focuses on making known machine learning algorithms fairer. Fair variants of classification, clustering, outlier detection and other styles of algorithms exist. However, an understudie…

Classification Clustering Fairness Outlier Detection