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 …
Querywise fair learning to rank through multi-objective optimization
In Learning-to-Rank (LTR) problems, the task of delivering relevant search results and
allocating fair exposure to items of a protected group can conflict. Previous works in Fair LTR …
allocating fair exposure to items of a protected group can conflict. Previous works in Fair LTR …
Curse of" low" dimensionality in recommender systems
N Ohsaka, R Togashi - Proceedings of the 46th International ACM SIGIR …, 2023 - dl.acm.org
Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such
as diversity, fairness, and robustness. We argue that many of the prevalent problems in …
as diversity, fairness, and robustness. We argue that many of the prevalent problems in …
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but
they are often oblivious to their impact on the items (eg, products, jobs, news, video) and …
they are often oblivious to their impact on the items (eg, products, jobs, news, video) and …
Search and Society: Reimagining Information Access for Radical Futures
B Mitra - arXiv preprint arXiv:2403.17901, 2024 - arxiv.org
Information retrieval (IR) technologies and research are undergoing transformative changes.
It is our perspective that the community should accept this opportunity to re-center our …
It is our perspective that the community should accept this opportunity to re-center our …
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as
healthcare, education and employment. Since they are used in numerous sensitive …
healthcare, education and employment. Since they are used in numerous sensitive …
Sampling individually-fair rankings that are always group fair
Rankings on online platforms help their end-users find the relevant information—people,
news, media, and products—quickly. Fair ranking tasks, which ask to rank a set of items to …
news, media, and products—quickly. Fair ranking tasks, which ask to rank a set of items to …
Fair matrix factorisation for large-scale recommender systems
Recommender systems are hedged with various requirements, such as ranking quality,
optimisation efficiency, and item fairness. Item fairness is an emerging yet impending issue …
optimisation efficiency, and item fairness. Item fairness is an emerging yet impending issue …
[PDF][PDF] FairRankTune: A User-Friendly Toolkit Supporting Fair Ranking Tasks
K CACHEL, E RUNDENSTEINER - 2023 - conference2023.eaamo.org
As this impactful area grows, a substantial obstacle faced by researchers is the unavailability
of rich fairness-relevant ranked data [36, 47, 70]. This is in part due to the challenge of …
of rich fairness-relevant ranked data [36, 47, 70]. This is in part due to the challenge of …
Sampling ex-post group-fair rankings
Randomized rankings have been of recent interest to achieve ex-ante fairer exposure and
better robustness than deterministic rankings. We propose a set of natural axioms for …
better robustness than deterministic rankings. We propose a set of natural axioms for …