When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

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 …

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

Recommender systems are the algorithms which select, filter, and personalize content
across many of the world's largest platforms and apps. As such, their positive and negative …

Adverse phenomena such as the search engine manipulation effect (SEME), where web
search users change their attitude on a topic following whatever most highly-ranked search …

The fair-ranking problem, which asks to rank a given set of items to maximize utility subject
to group fairness constraints, has received attention in the fairness, information retrieval, and …

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Algorithmic fairness is especially applicable in this domain due to its high stakes and …

Recent work on algorithmic fairness has largely focused on the fairness of discrete
decisions, or classifications. While such decisions are often based on risk score models, the …

Recommender systems, in real-world circumstances, tend to limit user exposure to certain
topics and to overexpose them to others to maximize performance. However, repeated …

We live in an information society that strongly relies on information retrieval systems, such as
search engines and conversational assistants. Consequently, the trustworthiness of these …