Fairness in ranking, part ii: Learning-to-rank and recommender systems
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 …
algorithmic rankers, with contributions coming from the data management, algorithms …
A survey on the fairness of recommender systems
Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …
and play an important role in people's daily lives. Since recommendations involve …
Fairness in ranking: A survey
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 …
algorithmic rankers, with contributions coming from the data management, algorithms …
Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and …
The increasing use of data-driven decision support systems in industry and governments is
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …
Fairness and transparency in recommendation: The users' perspective
Though recommender systems are defined by personalization, recent work has shown the
importance of additional, beyond-accuracy objectives, such as fairness. Because users often …
importance of additional, beyond-accuracy objectives, such as fairness. Because users often …
Algorithmic fairness datasets: the story so far
Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …
decisions, directly impacting people's well-being. As a result, a growing community of …
Provider fairness across continents in collaborative recommender systems
When a recommender system suggests items to the end-users, it gives a certain exposure to
the providers behind the recommended items. Indeed, the system offers a possibility to the …
the providers behind the recommended items. Indeed, the system offers a possibility to the …
Tackling documentation debt: a survey on algorithmic fairness datasets
A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …
advancing the understanding of risks and opportunities of automated decision-making for …
Enabling cross-continent provider fairness in educational recommender systems
With the widespread diffusion of Massive Online Open Courses (MOOCs), educational
recommender systems have become central tools to support students in their learning …
recommender systems have become central tools to support students in their learning …
Disparate impact in item recommendation: A case of geographic imbalance
Recommender systems are key tools to push items' consumption. Imbalances in the data
distribution can affect the exposure given to providers, thus affecting their experience in …
distribution can affect the exposure given to providers, thus affecting their experience in …