An Empirical Comparison of Bias Reduction Methods on Real-World Problems
in High-Stakes Policy Settings

An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

13 May 2021

Kit T. Rodolfa

Papers citing "An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings"

5 / 5 papers shown

Title
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation Sérgio Jesus José P. Pombal Duarte M. Alves André F. Cruz Pedro Saleiro Rita P. Ribeiro João Gama P. Bizarro 35 32 0 24 Nov 2022
A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions Ka Ho Brian Chor Kit T. Rodolfa Rayid Ghani 25 0 0 12 Jul 2022
ABCinML: Anticipatory Bias Correction in Machine Learning Applications Abdulaziz A. Almuzaini C. Bhatt David M. Pennock V. Singh FaML 22 10 0 14 Jun 2022
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 311 4,203 0 23 Aug 2019
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 201 2,082 0 24 Oct 2016