The convenient access to copious multifaceted data has encouraged machine learning
researchers to reconsider correlation-based learning and embrace the opportunity of …

Addressing the problem of fairness is crucial to safely use machine learning algorithms to
support decisions with a critical impact on people's lives such as job hiring, child …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …

Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …

Machine learning models have had discernible achievements in a myriad of applications.
However, most of these models are black-boxes, and it is obscure how the decisions are …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

Recent work highlights the role of causality in designing equitable decision-making
algorithms. It is not immediately clear, however, how existing causal conceptions of fairness …

Algorithmic bias and fairness in the context of graph mining have largely remained nascent.
The sparse literature on fair graph mining has almost exclusively focused on group-based …

Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …