With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

Algorithmic fairness has attracted significant attention in recent years, with many quantitative
measures suggested for characterizing the fairness of different machine learning algorithms …

Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between
different groups in downstream applications. A naive solution is to transform the data so that …

Demographic parity is the most widely recognized measure of group fairness in machine
learning, which ensures equal treatment of different demographic groups. Numerous works …

The widespread use of Artificial Intelligence (AI) in consequential domains, such as
healthcare and parole decision-making systems, has drawn intense scrutiny on the fairness …

The rapid use and success of deep learning models in various application domains have
raised significant challenges about the fairness of these models when used in the real world …

Understanding the cumulative effect of multiple fairness-enhancing interventions at different
stages of the machine learning (ML) pipeline is a critical and underexplored facet of the …

With the rise of machine learning, people are being increasingly impacted by algorithms that
are getting deployed to different areas including high-stake domains like education …

Artificial Intelligence (AI) systems are proliferating in our society due to their capacity to
simulate human intelligence, behaviors, and processes. The increased utilization of AI …

Abstract Machine Learning (ML) decision-making algorithms are now widely used in
predictive decision-making, for example, to determine who to admit and give a loan. Their …