Measuring Unfairness through Game-Theoretic Interpretability

12 October 2019

Papers citing "Measuring Unfairness through Game-Theoretic Interpretability"

8 / 8 papers shown

Title
Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration Vasiliki Papanikou Danae Pla Karidi E. Pitoura Emmanouil Panagiotou Eirini Ntoutsi 156 0 0 01 May 2025
Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks R. Ramachandranpillai Md Fahim Sikder David Bergstrom Fredrik Heintz SyDa 85 6 0 21 Apr 2024
A Critical Survey on Fairness Benefits of Explainable AI Luca Deck Jakob Schoeffer Maria De-Arteaga Niklas Kühl 115 13 0 15 Oct 2023
Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps Isel Grau Gonzalo Nápoles Fabian Hoitsma Lisa Koutsoviti Koumeri K. Vanhoof FAtt 49 0 0 16 May 2023
Fair Enough: Searching for Sufficient Measures of Fairness Suvodeep Majumder Joymallya Chakraborty Gina R. Bai Kathryn T. Stolee Tim Menzies 84 29 0 25 Oct 2021
Algorithm Fairness in AI for Medicine and Healthcare Richard J. Chen Tiffany Y. Chen Jana Lipkova Judy J. Wang Drew F. K. Williamson Ming Y. Lu S. Sahai Faisal Mahmood FaML 141 47 0 01 Oct 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset Chuizheng Meng Loc Trinh Nan Xu Yan Liu 67 30 0 12 Feb 2021
Fairness by Explicability and Adversarial SHAP Learning James M. Hickey Pietro G. Di Stefano V. Vasileiou FAtt FedML 123 19 0 11 Mar 2020