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 …

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) …

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 …

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 …

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

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 algorithms are used to make decisions in various applications. These
algorithms rely on large amounts of sensitive individual information to work properly. Hence …

Artificial Intelligence (AI) has made its way into various scientific fields, providing astonishing
improvements over existing algorithms for a wide variety of tasks. In recent years, there have …

Algorithms learned from data are increasingly used for deciding many aspects in our life:
from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence …