Empirical observation of negligible fairness-accuracy trade-offs in
machine learning for public policy

Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy

5 December 2020

Kit T. Rodolfa

Papers citing "Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy"

10 / 10 papers shown

Title
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning Caleb J. S. Barr Olivia Erdelyi Paul D. Docherty Randolph C. Grace FaML 65 0 0 10 Nov 2024
Amazing Things Come From Having Many Good Models Cynthia Rudin Chudi Zhong Lesia Semenova Margo Seltzer Ronald E. Parr Jiachang Liu Srikar Katta Jon Donnelly Harry Chen Zachery Boner 26 23 0 05 Jul 2024
Resource-constrained Fairness Sofie Goethals Eoin Delaney Brent Mittelstadt Christopher Russell FaML 78 1 0 03 Jun 2024
Avoiding spurious correlations via logit correction Sheng Liu Xu Zhang Nitesh Sekhar Yue Wu Prateek Singhal C. Fernandez‐Granda 25 29 0 02 Dec 2022
Fairness Increases Adversarial Vulnerability Cuong Tran Keyu Zhu Ferdinando Fioretto Pascal Van Hentenryck 18 6 0 21 Nov 2022
A novel approach for Fair Principal Component Analysis based on eigendecomposition G. D. Pelegrina L. Duarte FaML 8 11 0 24 Aug 2022
Algorithmic Fairness in Business Analytics: Directions for Research and Practice Maria De-Arteaga Stefan Feuerriegel M. Saar-Tsechansky FaML 16 42 0 22 Jul 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
Survey on Fair Reinforcement Learning: Theory and Practice Pratik Gajane A. Saxena M. Tavakol George Fletcher Mykola Pechenizkiy FaML OffRL 30 13 0 20 May 2022
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 201 2,082 0 24 Oct 2016