Fairness in ranking, part i: Score-based ranking
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 Systematic Review of Fairness, Accountability, Transparency and Ethics in Information Retrieval
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 …
search engines and conversational assistants. Consequently, the trustworthiness of these …
The De-democratization of AI: Deep learning and the compute divide in artificial intelligence research
Increasingly, modern Artificial Intelligence (AI) research has become more computationally
intensive. However, a growing concern is that due to unequal access to computing power …
intensive. However, a growing concern is that due to unequal access to computing power …
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 …
Responsible data management
The need for responsible data management intensifies with the growing impact of data on
society. One central locus of the societal impact of data are Automated Decision Systems …
society. One central locus of the societal impact of data are Automated Decision Systems …
Mitigating bias in set selection with noisy protected attributes
A Mehrotra, LE Celis - Proceedings of the 2021 ACM conference on …, 2021 - dl.acm.org
Subset selection algorithms are ubiquitous in AI-driven applications, including, online
recruiting portals and image search engines, so it is imperative that these tools are not …
recruiting portals and image search engines, so it is imperative that these tools are not …
Fair ranking with noisy protected attributes
A Mehrotra, N Vishnoi - Advances in Neural Information …, 2022 - proceedings.neurips.cc
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 …
to group fairness constraints, has received attention in the fairness, information retrieval, and …
Causal intersectionality for fair ranking
In this paper we propose a causal modeling approach to intersectional fairness, and a
flexible, task-specific method for computing intersectionally fair rankings. Rankings are used …
flexible, task-specific method for computing intersectionally fair rankings. Rankings are used …
Maxmin-fair ranking: individual fairness under group-fairness constraints
D García-Soriano, F Bonchi - Proceedings of the 27th ACM SIGKDD …, 2021 - dl.acm.org
We study a novel problem of fairness in ranking aimed at minimizing the amount of
individual unfairness introduced when enforcing group-fairness constraints. Our proposal is …
individual unfairness introduced when enforcing group-fairness constraints. Our proposal is …