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

6 May 2022

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

40 / 40 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
Directly Optimizing Explanations for Desired Properties Hiwot Belay Tadesse Alihan Hüyük Weiwei Pan Finale Doshi-Velez FAtt 31 0 0 31 Oct 2024
ConLUX: Concept-Based Local Unified Explanations Junhao Liu Haonan Yu Xin Zhang FAtt LRM 23 0 0 16 Oct 2024
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data M. Velmurugan Chun Ouyang Yue Xu Renuka Sindhgatta B. Wickramanayake Catarina Moreira ELM LMTD XAI 14 0 0 30 Sep 2024
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 31 2 0 26 Jul 2024
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning Eura Nofshin Esther Brown Brian Lim Weiwei Pan Finale Doshi-Velez 29 0 0 31 May 2024
SIDEs: Separating Idealization from Deceptive Explanations in xAI Emily Sullivan 36 2 0 25 Apr 2024
Evaluating Physician-AI Interaction for Cancer Management: Paving the Path towards Precision Oncology Zeshan Hussain Barbara D. Lam Fernando A. Acosta-Perez I. Riaz Maia L. Jacobs Andrew J. Yee David Sontag 33 0 0 23 Apr 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
On Explaining Unfairness: An Overview Christos Fragkathoulas Vasiliki Papanikou Danae Pla Karidi E. Pitoura XAI FaML 19 2 0 16 Feb 2024
Understanding Disparities in Post Hoc Machine Learning Explanation Vishwali Mhasawade Salman Rahman Zoe Haskell-Craig R. Chunara 13 4 0 25 Jan 2024
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning Julien Ferry Ulrich Aivodji Sébastien Gambs Marie-José Huguet Mohamed Siala FaML 13 5 0 22 Dec 2023
NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs Maria Antoniak Aakanksha Naik Carla S. Alvarado Lucy Lu Wang Irene Y. Chen AILaw 13 14 0 19 Dec 2023
Error Discovery by Clustering Influence Embeddings Fulton Wang Julius Adebayo Sarah Tan Diego Garcia-Olano Narine Kokhlikyan 11 3 0 07 Dec 2023
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 42 2 0 07 Dec 2023
LUCID-GAN: Conditional Generative Models to Locate Unfairness Andres Algaba Carmen Mazijn Carina E. A. Prunkl J. Danckaert Vincent Ginis SyDa 24 1 0 28 Jul 2023
Towards an AI Accountability Policy Przemyslaw A. Grabowicz Nicholas Perello Yair Zick Nicholas Perello Yair Zick 12 0 0 25 Jul 2023
Simple Steps to Success: Axiomatics of Distance-Based Algorithmic Recourse Jenny Hamer Jake Valladares Vignesh Viswanathan Yair Zick 17 0 0 27 Jun 2023
Reason to explain: Interactive contrastive explanations (REASONX) Laura State Salvatore Ruggieri Franco Turini LRM 14 1 0 29 May 2023
Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit M. Yang Gloria Hyunjung Kwak Tom Pollard L. A. Celi Marzyeh Ghassemi 14 10 0 22 May 2023
MLHOps: Machine Learning for Healthcare Operations Kristoffer Larsen Vallijah Subasri A. Krishnan Cláudio Tinoco Mesquita Diana Paez Laleh Seyyed-Kalantari Amalia Peix LM&MA AI4TS VLM 16 2 0 04 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
Feature Importance Disparities for Data Bias Investigations Peter W. Chang Leor Fishman Seth Neel 6 1 0 03 Mar 2023
On the Impact of Explanations on Understanding of Algorithmic Decision-Making Timothée Schmude Laura M. Koesten Torsten Moller Sebastian Tschiatschek 14 15 0 16 Feb 2023
Tensions Between the Proxies of Human Values in AI Teresa Datta D. Nissani Max Cembalest Akash Khanna Haley Massa John P. Dickerson 11 2 0 14 Dec 2022
"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations André Artelt Barbara Hammer FaML 13 8 0 27 Nov 2022
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
A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis Laurent Risser Agustin Picard Lucas Hervier Jean-Michel Loubes FaML 25 5 0 10 Oct 2022
LUCID: Exposing Algorithmic Bias through Inverse Design Carmen Mazijn Carina E. A. Prunkl Andres Algaba J. Danckaert Vincent Ginis SyDa 19 4 0 26 Aug 2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations Chirag Agarwal Dan Ley Satyapriya Krishna Eshika Saxena Martin Pawelczyk Nari Johnson Isha Puri Marinka Zitnik Himabindu Lakkaraju XAI 6 140 0 22 Jun 2022
Saliency Cards: A Framework to Characterize and Compare Saliency Methods Angie Boggust Harini Suresh Hendrik Strobelt John Guttag Arvindmani Satyanarayan FAtt XAI 16 8 0 07 Jun 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 56 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 4,187 0 23 Aug 2019
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 210 669 0 17 Feb 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,082 0 24 Oct 2016