Empirical Risk Minimization Under Fairness Constraints

Authors:
Michele Donini Istituto Italiano di Tecnologia
Luca Oneto University of Genoa
Shai Ben-David Universitys of Waterloo
John Shawe-Taylor UCL
Massimiliano Pontil IIT

Introduction:

The authors address the problem of algorithmic fairness: ensuring that sensitive information does not unfairly influence the outcome of a classifier.The authors present an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem.

Abstract:

We address the problem of algorithmic fairness: ensuring that sensitive information does not unfairly influence the outcome of a classifier. We present an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem. It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable. We derive both risk and fairness bounds that support the statistical consistency of our methodology. We specify our approach to kernel methods and observe that the fairness requirement implies an orthogonality constraint which can be easily added to these methods. We further observe that for linear models the constraint translates into a simple data preprocessing step. Experiments indicate that the method is empirically effective and performs favorably against state-of-the-art approaches.

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