The integration of machine learning (ML) systems into various sectors, notably education,
has great potential to transform business workflows and decision-making processes …

The pervasive integration of machine learning (ML) across various sectors has underscored
the critical challenge of addressing inherent biases in ML models. These biases not only …

In this paper, we interrogate whether data quality issues track demographic group
membership (based on sex, race and age) and whether automated data cleaning—of the …

Abstract Machine learning is part of the daily life of people and companies worldwide.
Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision …

Fairness concerns have recently been gaining increasing attention in machine learning (ML)
research and applications. ML models typically require massive data, which can be costly …

Content spread inequity is a potential unfairness issue in online social networks, disparately
impacting minority groups. In this paper, we view friendship suggestion, a common feature in …

Conditional independence (CI) constraints are critical for defining and evaluating fairness in
machine learning as well as for learning unconfounded or causal representations …

Causal inference is a fundamental concept that goes beyond simple correlation and model-
based prediction analysis, and is highly relevant in domains such as health, medicine, and …

Abstract Machine learning methods based on AdaBoost have been widely applied to
various classification problems across many mission-critical applications including …

This paper proposes a federated, fair, and fast k-means algorithm (F3KM) to solve the fair
clustering problem efficiently in scenarios where data cannot be shared among different …