Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy

24 January 2022

Papers citing "Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy"

6 / 6 papers shown

Title
Learning Optimal Classification Trees Robust to Distribution Shifts Nathan Justin S. Aghaei Andrés Gómez P. Vayanos OOD 33 0 0 26 Oct 2023
Fairness guarantee in multi-class classification Christophe Denis Romuald Elie Mohamed Hebiri Franccois Hu FaML 28 47 0 28 Sep 2021
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction Caroline Linjun Wang Bin Han Bhrij Patel Cynthia Rudin FaML HAI 57 83 0 08 May 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,187 0 23 Aug 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,082 0 24 Oct 2016