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"

10 / 10 papers shown

Title
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods Mahdi Dhaini Ege Erdogan Nils Feldhus Gjergji Kasneci 46 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 56 2 0 25 Apr 2024
Accurate estimation of feature importance faithfulness for tree models Mateusz Gajewski Adam Karczmarz Mateusz Rapicki Piotr Sankowski 37 0 0 04 Apr 2024
Procedural Fairness in Machine Learning Ziming Wang Changwu Huang Xin Yao FaML 39 0 0 02 Apr 2024
On Explaining Unfairness: An Overview Christos Fragkathoulas Vasiliki Papanikou Danae Pla Karidi E. Pitoura XAI FaML 19 2 0 16 Feb 2024
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations Vinitra Swamy Jibril Frej Tanja Kaser 23 14 0 01 Jul 2023
Tensions Between the Proxies of Human Values in AI Teresa Datta D. Nissani Max Cembalest Akash Khanna Haley Massa John P. Dickerson 34 2 0 14 Dec 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 38 77 0 06 May 2022
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 136 119 0 21 Jan 2021
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017