Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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 …

Research on the ethics of algorithms has grown substantially over the past decade.
Alongside the exponential development and application of machine learning algorithms …

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 …

This article presents the first, systematic analysis of the ethical challenges posed by
recommender systems through a literature review. The article identifies six areas of concern …

Disparate impact has raised serious concerns in machine learning applications and its
societal impacts. In response to the need of mitigating discrimination, fairness has been …

Ride hailing platforms, such as Uber, Lyft, Ola or DiDi, have traditionally focused on the
satisfaction of the passengers, or on boosting successful business transactions. However …

Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …

In traditional recommender system literature, diversity is often seen as the opposite of
similarity, and typically defined as the distance between identified topics, categories or word …

Ranking items or people is a fundamental operation at the basis of several processes and
services, not all of them happening online. Ranking is required for different tasks, including …