Fair Wrapping for Black-box Predictions
Abstract
We introduce a new family of techniques to post-process an accurate black box posterior and reduce its bias, born out of the recent analysis of improper loss functions whose optimisation can correct any \textit{twist} in prediction, unfairness being treated as one. Post-processing involves learning a function we define as an $\alpha$-tree for the correction, for which we provide two generic boosting compliant training algorithms. We show that our correction has appealing properties in terms of composition of corrections, generalization, interpretability and divergence to the black box. We exemplify the use of our technique for fairness compliance in three models: conditional value at risk, equality of opportunity and statistical parity and provide experiments on several readily available domains.
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