Nudging, a concept rooted in behavioural economics, aims to influence decision-making for
individuals' benefit through non-coercive methods. While initially investigated in real-world …

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 …

Diversity is a crucial precondition for a democratic public discourse. In today's high-choice
media environments, exposure to diverse news is largely determined by individuals' …

Amidst the rampant use of algorithmization enabled by AI, the common theme of AI systems
is the human factor. Humans play an essential role in designing, developing, and …

Few would disagree that AI systems and applications need to be “responsible,” but what is
“responsible” and how to answer that question? Answering that question requires a …

This paper focuses on the opportunities and the ethical and societal risks posed by
advanced AI assistants. We define advanced AI assistants as artificial agents with natural …

Research has revealed that the diffusion of misinformation online can be attributed to
inaccuracies in human cognitive reasoning. We examine the nudging effect of Accuracy …

The developing research area of digital nudges has garnered increasing attention over the
past decade. However, with the acceleration of technological change, there is a need to …

Algorithmic technologies are dominating our online experiences, from content
recommendations to personalized services. However, it also introduces a new challenge …

Under which conditions do news recommender systems (NRSs) amplify or reduce selective
exposure? I provide the Recommender Influenced Selective Exposure framework, which …