# Fairness Constraints: A Mechanism for Fair Classification

**Authors:** Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi  
**arXiv:** [1507.05259](https://arxiv.org/abs/1507.05259)  
**Date:** 2015-07-19  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/1507.05259v1/

## Abstract

Automated data-driven decision systems are ubiquitous across a wide variety of online services, from online social networking and e-commerce to e-government. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead to user discrimination, even in the absence of intent. In this paper, we introduce fairness constraints, a mechanism to ensure fairness in a wide variety of classifiers in a principled manner. Fairness prevents a classifier from outputting predictions correlated with certain sensitive attributes in the data. We then instantiate fairness constraints on three well-known classifiers -- logistic regression, hinge loss and support vector machines (SVM) -- and evaluate their performance in a real-world dataset with meaningful sensitive human attributes. Experiments show that fairness constraints allow for an optimal trade-off between accuracy and fairness.