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 …

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

Motivated by settings in which predictive models may be required to be non-discriminatory
with respect to certain attributes (such as race), but even collecting the sensitive attribute …

As machine learning algorithms are deployed on sensitive data in critical decision making
processes, it is becoming increasingly important that they are also private and fair. In this …

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 …

Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and
trustworthy Machine Learning (ML). Each objective has been independently studied in the …

We study the relationship between two desiderata of algorithms in statistical inference and
machine learning—differential privacy and robustness to adversarial data corruptions. Their …

Abstract Machine learning models are increasingly used in high-stakes decision-making
systems. In such applications, a major concern is that these models sometimes discriminate …

We propose differential fairness, a multi-attribute definition of fairness in machine learning
which is informed by intersectionality, a critical lens arising from the humanities literature …

Fairness and privacy are both significant social norms in machine learning. In (Hu et al
2019), we propose a distributed framework to learn fair prediction models while protecting …