Deploying machine learning (ML) models often requires both fairness and privacy
guarantees. Both of these objectives present unique trade-offs with the utility (eg, accuracy) …

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 …

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 …

Machine learning techniques are increasingly used for high-stakes decision-making, such
as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure …

As machine learning (ML) becomes more prevalent in human-centric applications, there is a
growing emphasis on algorithmic fairness and privacy protection. While previous research …

Machine learning models are deployed as a central component in decision making and
policy operations with direct impact on individuals' lives. In order to act ethically and comply …

As deep learning models mature, one of the most prescient questions we face is: what is the
ideal tradeoff between accuracy, fairness, and privacy (AFP)? Unfortunately, both the privacy …

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential
privacy (DP) during model training. Practical DP-ensuring training methods use …

This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It
reviews the conditions under which privacy and fairness may have aligned or contrasting …

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 …