Empirical Risk Minimization under Fairness Constraints

Understand

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.