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Fairness Certificates for Differentially Private Classification
Paul Mangold · Michaël Perrot · Marc Tommasi · Aurélien Bellet

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in Algorithmic Fairness through the Lens of Causality and Privacy »

In this work, we theoretically study the impact of differential privacy on fairness in binary classification. We prove that, given a class of models, popular group fairness measures are pointwise Lipschitz-continuous with respect to the parameters of the model. This result is a consequence of a more general statement on the probability that a decision function makes a negative prediction conditioned on an arbitrary event (such as membership to a sensitive group), which may be of independent interest. We use the aforementioned Lipschitz property to prove a high probability bound showing that, given enough examples, the fairness level of private models is close to the one of their non-private counterparts.

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Author Information

Paul Mangold (Inria Lille)
Michaël Perrot (INRIA)
Marc Tommasi (INRIA)
Aurélien Bellet (INRIA)

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