How to be fair? A study of label and selection bias

21 March 2024

Papers citing "How to be fair? A study of label and selection bias"

5 / 5 papers shown

Title
Correcting Annotator Bias in Training Data: Population-Aligned Instance Replication (PAIR) Stephanie Eckman Bolei Ma Christoph Kern Rob Chew Yun Xue Frauke Kreuter 41 0 0 12 Jan 2025
Cherry on the Cake: Fairness is NOT an Optimization Problem Marco Favier T. Calders 23 1 0 24 Jun 2024
The Pursuit of Fairness in Artificial Intelligence Models: A Survey Tahsin Alamgir Kheya Mohamed Reda Bouadjenek Sunil Aryal 36 8 0 26 Mar 2024
Towards the Right Kind of Fairness in AI Boris Ruf Marcin Detyniecki 58 26 0 16 Feb 2021
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 341 4,230 0 23 Aug 2019