Fairness Certificates for Differentially Private Classification. (arXiv:2210.16242v1 [cs.LG])

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 …

certificates classification fairness