  We address the problem of algorithmic fairness: ensuring that sensitive
variables do 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 approach. 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.
