Recommender systems can strongly influence which information we see online, eg, on
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the
fairness of such data-driven insights remains a concern in high-stakes fields. Despite …

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 …

In recent years, the problem of addressing fairness in machine learning (ML) and automatic
decision making has attracted a lot of attention in the scientific communities dealing with …

In recent years a substantial literature has emerged concerning bias, discrimination, and
fairness in artificial intelligence (AI) and machine learning. Connecting this work to existing …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Algorithmic impact assessments (AIAs) are an emergent form of accountability for
organizations that build and deploy automated decision-support systems. They are modeled …

Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …

Fair machine learning aims to mitigate the biases of model predictions against certain
subpopulations regarding sensitive attributes such as race and gender. Among the many …

Recent discoveries have revealed that deep neural networks might behave in a biased
manner in many real-world scenarios. For instance, deep networks trained on a large-scale …