We propose a scheme for auditing differentially private machine learning systems with a
single training run. This exploits the parallelism of being able to add or remove multiple …

Kearns, Neel, Roth, and Wu [ICML 2018] recently proposed a notion of rich subgroup
fairness intended to bridge the gap between statistical and individual notions of fairness …

This work studies the problem of privacy-preserving classification–namely, learning a
classifier from sensitive data while preserving the privacy of individuals in the training set. In …

We consider the privacy-preserving machine learning (ML) setting where the trained model
must satisfy differential privacy (DP) with respect to the labels of the training examples. We …

A critical concern in data-driven decision making is to build models whose outcomes do not
discriminate against some demographic groups, including gender, ethnicity, or age. To …

The traditional notion of\emphgeneralization—ie, learning a hypothesis whose empirical
error is close to its true error—is surprisingly brittle. As has recently been noted [Dwork et al …

We study fair classification in the presence of an omniscient adversary that, given an $\eta $,
is allowed to choose an arbitrary $\eta $-fraction of the training samples and arbitrarily …

Because learning sometimes involves sensitive data, standard machine-learning algorithms
have been extended to offer strong privacy guarantees for training data. However, in …

As applications of machine learning become increasingly widespread, the need to ensure
model accuracy and fairness while protecting the privacy of user data becomes more …

Using machine learning models in critical decision-making processes has given rise to a call
for algorithmic transparency. Model explanations, however, might leak information about the …