The Road to Explainability is Paved with Bias: Measuring the Fairness of
Explanations

The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations

6 May 2022

Aparna Balagopalan

Thomas Hartvigsen

Marzyeh Ghassemi

Papers citing "The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations"

16 / 16 papers shown

Title
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods Mahdi Dhaini Ege Erdogan Nils Feldhus Gjergji Kasneci 39 0 0 02 May 2025
In defence of post-hoc explanations in medical AI Joshua Hatherley Lauritz Munch Jens Christian Bjerring 26 0 0 29 Apr 2025
A Catalog of Fairness-Aware Practices in Machine Learning Engineering Gianmario Voria Giulia Sellitto Carmine Ferrara Francesco Abate A. Lucia F. Ferrucci Gemma Catolino Fabio Palomba FaML 29 3 0 29 Aug 2024
To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling S. Rutherford T. Wolfers Charlotte J. Fraza Nathaniel G. Harrnet Christian F. Beckmann H. Ruhé A. Marquand CML 29 2 0 26 Jul 2024
From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap Tianqi Kou 32 0 0 19 Apr 2024
Accurate estimation of feature importance faithfulness for tree models Mateusz Gajewski Adam Karczmarz Mateusz Rapicki Piotr Sankowski 27 0 0 04 Apr 2024
LUCID-GAN: Conditional Generative Models to Locate Unfairness Andres Algaba Carmen Mazijn Carina E. A. Prunkl J. Danckaert Vincent Ginis SyDa 21 1 0 28 Jul 2023
Reason to explain: Interactive contrastive explanations (REASONX) Laura State Salvatore Ruggieri Franco Turini LRM 11 1 0 29 May 2023
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK L. Nannini Agathe Balayn A. Smith 6 37 0 20 Apr 2023
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations Zixi Chen Varshini Subhash Marton Havasi Weiwei Pan Finale Doshi-Velez XAI FAtt 14 17 0 10 Nov 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Jessica Dai Sohini Upadhyay Ulrich Aivodji Stephen H. Bach Himabindu Lakkaraju 35 55 0 15 May 2022
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction Caroline Linjun Wang Bin Han Bhrij Patel Cynthia Rudin FaML HAI 57 83 0 08 May 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,143 0 23 Aug 2019
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 210 663 0 17 Feb 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,658 0 28 Feb 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,079 0 24 Oct 2016