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Simpson's Paradox in Recommender Fairness: Reconciling differences
  between per-user and aggregated evaluations

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations

14 October 2022
Flavien Prost
Ben Packer
Jilin Chen
Li Wei
Pierre-Antoine Kremp
Nicholas Blumm
Susan Wang
Tulsee Doshi
Tonia Osadebe
Lukasz Heldt
Ed H. Chi
Alex Beutel
ArXivPDFHTML

Papers citing "Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations"

4 / 4 papers shown
Title
ComFairGNN: Community Fair Graph Neural Network
ComFairGNN: Community Fair Graph Neural Network
Yonas Sium
Qi Li
31
0
0
07 Nov 2024
Matched Pair Calibration for Ranking Fairness
Matched Pair Calibration for Ranking Fairness
Hannah Korevaar
Christopher McConnell
Edmund Tong
Erik Brinkman
Alana Shine
Misam Abbas
Blossom Metevier
S. Corbett-Davies
Khalid El-Arini
6
1
0
06 Jun 2023
The Flawed Foundations of Fair Machine Learning
The Flawed Foundations of Fair Machine Learning
R. Poe
Soumia Zohra El Mestari
FaML
27
1
0
02 Jun 2023
On the Within-Group Fairness of Screening Classifiers
On the Within-Group Fairness of Screening Classifiers
Nastaran Okati
Stratis Tsirtsis
Manuel Gomez Rodriguez
22
2
0
31 Jan 2023
1