On Explaining Unfairness: An Overview

16 February 2024

Christos Fragkathoulas

Papers citing "On Explaining Unfairness: An Overview"

24 / 24 papers shown

Title
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations Dan Ley Saumitra Mishra Daniele Magazzeni LRM 61 18 0 26 May 2023
Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations Yuying Zhao Yu Wang Hanyu Wang FaML 58 16 0 07 Dec 2022
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges Mario Alfonso Prado-Romero Bardh Prenkaj Giovanni Stilo F. Giannotti CML 88 32 0 21 Oct 2022
On Structural Explanation of Bias in Graph Neural Networks Yushun Dong Song Wang Yu Wang Hanyu Wang Jundong Li 65 24 0 24 Jun 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 62 78 0 06 May 2022
FMP: Toward Fair Graph Message Passing against Topology Bias Zhimeng Jiang Xiaotian Han Chao Fan Zirui Liu Na Zou Ali Mostafavi Xia Hu 40 48 0 08 Feb 2022
Interpretable Data-Based Explanations for Fairness Debugging Romila Pradhan Jiongli Zhu Boris Glavic Babak Salimi 44 54 0 17 Dec 2021
A survey on datasets for fairness-aware machine learning Tai Le Quy Arjun Roy Vasileios Iosifidis Wenbin Zhang Eirini Ntoutsi FaML 59 247 0 01 Oct 2021
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition Weishen Pan Sen Cui Jiang Bian Changshui Zhang Fei Wang CML FaML 84 35 0 11 Aug 2021
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks Yushun Dong Ninghao Liu B. Jalaeian Jundong Li 94 121 0 11 Aug 2021
A Clarification of the Nuances in the Fairness Metrics Landscape Alessandro Castelnovo Riccardo Crupi Greta Greco D. Regoli Ilaria Giuseppina Penco A. Cosentini FaML 42 186 0 01 Jun 2021
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals Sainyam Galhotra Romila Pradhan Babak Salimi CML 60 107 0 22 Mar 2021
The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer Maarten Buyl T. D. Bie 38 25 0 02 Mar 2021
Towards a Unified Framework for Fair and Stable Graph Representation Learning Chirag Agarwal Himabindu Lakkaraju Marinka Zitnik 162 160 0 25 Feb 2021
Benchmarking and Survey of Explanation Methods for Black Box Models F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 89 226 0 25 Feb 2021
Explainability for fair machine learning T. Begley Tobias Schwedes Christopher Frye Ilya Feige FaML FedML 80 47 0 14 Oct 2020
On the Fairness of Causal Algorithmic Recourse Julius von Kügelgen Amir-Hossein Karimi Umang Bhatt Isabel Valera Adrian Weller Bernhard Schölkopf FaML 104 85 0 13 Oct 2020
Fairness in Machine Learning: A Survey Simon Caton C. Haas FaML 90 635 0 04 Oct 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 44 340 0 14 Feb 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 116 6,251 0 22 Oct 2019
Operationalizing Individual Fairness with Pairwise Fair Representations Preethi Lahoti Krishna P. Gummadi Gerhard Weikum FaML 67 101 0 02 Jul 2019
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,954 0 06 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 98 2,350 0 01 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,815 0 22 May 2017