Fairness Constraints: A Mechanism for Fair Classification

Muhammad Bilal Zafar

Muhammad Bilal Zafar, Isabel Valera Martinez, Manuel Gomez Rodriguez, and Krishna Gummadi

Automated data-driven decision systems are ubiquitous across a wide variety of online services, from online social networking and ecommerce 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.