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 …

With the rising concerns over privacy and fairness in machine learning, privacy-preserving
fair machine learning has received tremendous attention in recent years. However, most …

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) …

In this article, we propose a new variational approach to learn private and/or fair
representations. This approach is based on the Lagrangians of a new formulation of the …

Privacy-preserving representation learning (PPRL) aims to learn a data encoding that
obfuscates sensitive information and retains target information. We develop the Exclusion …

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 models have demonstrated promising performance in many areas.
However, the concerns that they can be biased against specific demographic groups hinder …

Machine learning (ML) algorithms rely primarily on the availability of training data, and,
depending on the domain, these data may include sensitive information about the data …

The worldwide adoption of machine learning (ML) and deep learning models, particularly in
critical sectors, such as healthcare and finance, presents substantial challenges in …