Algorithms have increasingly been deployed to make consequential decisions, and there
have been many ethical questions raised about how these algorithms function. Three ethical …

The concerns of fairness, and privacy, in machine learning based systems have received a
lot of attention in the research community recently, but have primarily been studied in …

In this work, we investigate whether privacy and fairness can be simultaneously achieved by
a single classifier in several different models. Some of the earliest work on fairness in …

One of the main concerns about fairness in machine learning (ML) is that, in order to achieve
it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. proposed …

We address the problem of randomized learning and generalization of fair and private
classifiers. From one side we want to ensure that sensitive information does not unfairly …

The problem of fair machine learning has drawn much attention over the last few years and
the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also …

Online learning, in the mistake bound model, is one of the most fundamental concepts in
learning theory and differential privacy is, perhaps, the most widely used statistical concept …

One of the main concerns about fairness in machine learning (ML) is that, in order to achieve
it, one may have to trade off some accuracy. To overcome this issue, Hardt et al.(Adv Neural …

In this work, we look at cases where we want a classifier to be both fair and interpretable,
and find that it is necessary to make trade-offs between these two properties. We have …

We theoretically study the impact of differential privacy on fairness in classification. We prove
that, given a class of models, popular group fairness measures are pointwise Lipschitz …