A snapshot of the frontiers of fairness in machine learning
A Chouldechova, A Roth - Communications of the ACM, 2020 - dl.acm.org
A snapshot of the frontiers of fairness in machine learning Page 1 82 COMMUNICATIONS OF
THE ACM | MAY 2020 | VOL. 63 | NO. 5 review articles ILL US TRA TION B Y JUS TIN METZ …
THE ACM | MAY 2020 | VOL. 63 | NO. 5 review articles ILL US TRA TION B Y JUS TIN METZ …
Filter bubbles in recommender systems: Fact or fallacy—A systematic review
A filter bubble refers to the phenomenon where Internet customization effectively isolates
individuals from diverse opinions or materials, resulting in their exposure to only a select set …
individuals from diverse opinions or materials, resulting in their exposure to only a select set …
Towards long-term fairness in recommendation
As Recommender Systems (RS) influence more and more people in their daily life, the issue
of fairness in recommendation is becoming more and more important. Most of the prior …
of fairness in recommendation is becoming more and more important. Most of the prior …
The frontiers of fairness in machine learning
A Chouldechova, A Roth - arXiv preprint arXiv:1810.08810, 2018 - arxiv.org
The last few years have seen an explosion of academic and popular interest in algorithmic
fairness. Despite this interest and the volume and velocity of work that has been produced …
fairness. Despite this interest and the volume and velocity of work that has been produced …
Fairness-aware explainable recommendation over knowledge graphs
There has been growing attention on fairness considerations recently, especially in the
context of intelligent decision making systems. For example, explainable recommendation …
context of intelligent decision making systems. For example, explainable recommendation …
Classification with fairness constraints: A meta-algorithm with provable guarantees
Developing classification algorithms that are fair with respect to sensitive attributes of the
data is an important problem due to the increased deployment of classification algorithms in …
data is an important problem due to the increased deployment of classification algorithms in …
A survey on trustworthy recommender systems
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …
deployed in almost every corner of the web and facilitate the human decision-making …
Ranking with fairness constraints
LE Celis, D Straszak, NK Vishnoi - arXiv preprint arXiv:1704.06840, 2017 - arxiv.org
Ranking algorithms are deployed widely to order a set of items in applications such as
search engines, news feeds, and recommendation systems. Recent studies, however, have …
search engines, news feeds, and recommendation systems. Recent studies, however, have …
Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions
Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …
companies by directly affecting their key performance indicators. Nowadays, in this era of big …
Explainable fairness in recommendation
Existing research on fairness-aware recommendation has mainly focused on the
quantification of fairness and the development of fair recommendation models, neither of …
quantification of fairness and the development of fair recommendation models, neither of …