Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in
many real-world application domains where decisions can have a strong societal impact …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

The growing importance of understanding and addressing algorithmic bias in artificial
intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the …

The influence maximization problem (IMP) has been one of the most attractive topics in the
field of social networks. However, sometimes fairness in IMP should be considered …

In today's increasingly connected world, graph mining plays a pivotal role in many real-world
application domains, including social network analysis, recommendations, marketing and …

In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …

Abstract This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts
and defines terminology in the field of adversarial machine learning (AML). The taxonomy is …

Poor diets are a leading cause of morbidity and mortality. Exposure to low-quality food
environments saturated with fast food outlets is hypothesized to negatively impact diet …

Group fairness is a popular approach to prevent unfavorable treatment of individuals based
on sensitive attributes such as race, gender, and disability. However, the reliance of group …

We study the problem of learning, from observational data, fair and interpretable policies that
effectively match heterogeneous individuals to scarce resources of different types. We model …