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Controlling bias in training datasets is vital for ensuring equal treatment,or parity, between different groups in downstream applications. A naivesolution is to transform the data so that it is statistically independent ofgroup membership, but this may throw away too much information when areasonable compromise between fairness and accuracy is desired. Another commonapproach is to limit the ability of a particular adversary who seeks tomaximize parity. Unfortunately, representations produced by adversarialapproaches may still retain biases as their efficacy is tied to the complexityof the adversary used during training. To this end, we theoretically establishthat by limiting the mutual information between representations and protectedattributes, we can assuredly control the parity of any downstream classifier.We demonstrate an effective method for controlling parity through mutualinformation based on contrastive information estimators and show that theyoutperform approaches that rely on variational bounds based on complexgenerative models. We test our approach on UCI Adult and Heritage Healthdatasets and demonstrate that our approach provides more informativerepresentations across a range of desired parity thresholds while providingstrong theoretical guarantees on the parity of any downstream algorithm.

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation