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

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 …

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 …

Privacy protection for personal data and fairness in automated decisions are fundamental
requirements for responsible Machine Learning. Both may be enforced through data …

Fairness concerns about algorithmic decision-making systems have been mainly focused on
the outputs (eg, the accuracy of a classifier across individuals or groups). However, one may …

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 …

The availability of large datasets and computational resources has driven significant
progress in Artificial Intelligence (AI) and, especially, Machine Learning (ML). These …

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

Supervised learning models have been increasingly used for making decisions about
individuals in applications such as hiring, lending, and college admission. These models …

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) …