Machine learning fairness notions: Bridging the gap with real-world applications

30 June 2020

Papers citing "Machine learning fairness notions: Bridging the gap with real-world applications"

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 70 0 0 10 Nov 2024
(Local) Differential Privacy has NO Disparate Impact on Fairness Héber H. Arcolezi K. Makhlouf C. Palamidessi 32 6 0 25 Apr 2023
An Empirical Analysis of Fairness Notions under Differential Privacy Anderson Santana de Oliveira Caelin Kaplan Khawla Mallat Tanmay Chakraborty FedML 13 7 0 06 Feb 2023
Equality of Effort via Algorithmic Recourse Francesca Raimondi A. Lawrence Hana Chockler 23 1 0 21 Nov 2022
Explainable Global Fairness Verification of Tree-Based Classifiers Stefano Calzavara Lorenzo Cazzaro Claudio Lucchese Federico Marcuzzi 24 2 0 27 Sep 2022
Causal Discovery for Fairness Ruta Binkyt.e-Sadauskien.e K. Makhlouf Carlos Pinzón Sami Zhioua C. Palamidessi CML 27 16 0 14 Jun 2022
Accurate Fairness: Improving Individual Fairness without Trading Accuracy Xuran Li Peng Wu Jing Su FaML 33 17 0 18 May 2022
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 251 488 0 31 Dec 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,203 0 23 Aug 2019
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,082 0 24 Oct 2016