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 the frontier of machine learning applications moves further into human interaction,
multiple concerns arise regarding automated decision-making. Two of the most critical …

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 …

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 …

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 …

This paper considers the need for generalizable bias mitigation techniques in machine
learning due to the growing concerns of fairness and discrimination in data-driven decision …

Given the increasing importance of machine learning (ML) in our lives, several algorithmic
fairness techniques have been proposed to mitigate biases in the outcomes of the ML …

Machine learning (ML) has become a critical tool in public health, offering the potential to
improve population health, diagnosis, treatment selection, and health system efficiency …

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases
in historical data used to train them. While computational techniques are emerging to …