In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

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

Recommender systems typically retrieve items from an item corpus for personalized
recommendations. However, such a retrieval-based recommender paradigm faces two …

In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

When a model's performance differs across socially or culturally relevant groups–like race,
gender, or the intersections of many such groups–it is often called” biased.” While much of …

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 …

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 …

Information access research (and development) sometimes makes use of gender, whether
to report on the demographics of participants in a user study, as inputs to personalized …

Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based
on the idea that all items or item groups should get exposure proportional to the merit of the …

Societal biases that are contained in retrieved documents have received increased interest.
Such biases, which are often prevalent in the training data and learned by the model, can …