Fairness via Explanation Quality: Evaluating Disparities in the Quality
of Post hoc Explanations

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations

15 May 2022

Sohini Upadhyay

Stephen H. Bach

Himabindu Lakkaraju

Papers citing "Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations"

5 / 5 papers shown

Title
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods Mahdi Dhaini Ege Erdogan Nils Feldhus Gjergji Kasneci 17 0 0 02 May 2025
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients Evandro S. Ortigossa Fábio F. Dias Brian Barr Claudio T. Silva L. G. Nonato FAtt 36 2 0 25 Apr 2024
Accurate estimation of feature importance faithfulness for tree models Mateusz Gajewski Adam Karczmarz Mateusz Rapicki Piotr Sankowski 19 0 0 04 Apr 2024
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations Sérgio Jesus Catarina Belém Vladimir Balayan João Bento Pedro Saleiro P. Bizarro João Gama 109 103 0 21 Jan 2021
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 219 2,098 0 28 Feb 2017