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Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications

7 March 2021

Papers citing "Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications"

50 / 67 papers shown

Title
Investigating the Duality of Interpretability and Explainability in Machine Learning Moncef Garouani Josiane Mothe Ayah Barhrhouj Julien Aligon AAML 96 3 0 27 Mar 2025
What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems Qiushuo Hou Sangwoo Park Matteo Zecchin Yunlong Cai Guanding Yu Osvaldo Simeone 436 1 0 30 Sep 2024
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making Md. Naimul Hoque Klaus Mueller CML 86 30 0 03 Jan 2021
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach M. Velmurugan Chun Ouyang Catarina Moreira Renuka Sindhgatta XAI 79 5 0 08 Dec 2020
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions Jiaxuan Wang Jenna Wiens Scott M. Lundberg FAtt 92 90 0 27 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 70 172 0 08 Oct 2020
Principles and Practice of Explainable Machine Learning Vaishak Belle I. Papantonis FaML 62 447 0 18 Sep 2020
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models Furui Cheng Yao Ming Huamin Qu CML HAI 32 101 0 19 Aug 2020
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models Catarina Moreira Yu-Liang Chou M. Velmurugan Chun Ouyang Renuka Sindhgatta P. Bruza 90 58 0 21 Jul 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 126 86 0 23 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 155 603 0 16 Jun 2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi Julius von Kügelgen Bernhard Schölkopf Isabel Valera CML 104 180 0 11 Jun 2020
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI) Mark T. Keane Barry Smyth CML 68 146 0 26 May 2020
Multi-Objective Counterfactual Explanations Susanne Dandl Christoph Molnar Martin Binder B. Bischl 62 259 0 23 Apr 2020
What is "Intelligent" in Intelligent User Interfaces? A Meta-Analysis of 25 Years of IUI Sarah Theres Volkel Christina Schneegass Malin Eiband Daniel Buschek 52 41 0 06 Mar 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 91 96 0 05 Mar 2020
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference M. Usaid Awan Marco Morucci Vittorio Orlandi Sudeepa Roy Cynthia Rudin A. Volfovsky CML 61 13 0 02 Mar 2020
The Pragmatic Turn in Explainable Artificial Intelligence (XAI) Andrés Páez 66 196 0 22 Feb 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space Riccardo Guidotti A. Monreale Stan Matwin D. Pedreschi FAtt 87 67 0 27 Jan 2020
Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations Andreas Holzinger André M. Carrington Heimo Muller LRM XAI ELM 66 307 0 19 Dec 2019
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches Kacper Sokol Peter A. Flach XAI 100 302 0 11 Dec 2019
Counterfactual Explanation Algorithms for Behavioral and Textual Data Yanou Ramon David Martens F. Provost Theodoros Evgeniou FAtt 103 88 0 04 Dec 2019
PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems Azin Ghazimatin Oana Balalau Rishiraj Saha Roy Gerhard Weikum FAtt 66 99 0 19 Nov 2019
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 121 6,269 0 22 Oct 2019
Learning Model-Agnostic Counterfactual Explanations for Tabular Data Martin Pawelczyk Johannes Haug Klaus Broelemann Gjergji Kasneci OOD CML 62 203 0 21 Oct 2019
FACE: Feasible and Actionable Counterfactual Explanations Rafael Poyiadzi Kacper Sokol Raúl Santos-Rodríguez T. D. Bie Peter A. Flach 71 369 0 20 Sep 2019
Measurable Counterfactual Local Explanations for Any Classifier Adam White Artur Garcez FAtt 61 99 0 08 Aug 2019
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 61 198 0 22 Jul 2019
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting Ana Lucic H. Haned Maarten de Rijke 66 63 0 17 Jul 2019
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 79 495 0 09 Jul 2019
A Human-Grounded Evaluation of SHAP for Alert Processing Hilde J. P. Weerts Werner van Ipenburg Mykola Pechenizkiy FAtt 50 70 0 07 Jul 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 75 385 0 03 Jul 2019
Generating Counterfactual and Contrastive Explanations using SHAP Shubham Rathi 57 57 0 21 Jun 2019
Is Attention Interpretable? Sofia Serrano Noah A. Smith 108 684 0 09 Jun 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 91 321 0 27 May 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 115 1,021 0 19 May 2019
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations Hui Fen Tan Kuangyan Song Yiming Sun Yujia Zhang Madeilene Udell FAtt 108 19 0 29 Apr 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 102 1,015 0 26 Feb 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 195 1,441 0 14 Jan 2019
Efficient Search for Diverse Coherent Explanations Chris Russell 64 238 0 02 Jan 2019
Interpretable Credit Application Predictions With Counterfactual Explanations Rory Mc Grath Luca Costabello Chan Le Van Paul Sweeney F. Kamiab Zhao Shen Freddy Lecue FAtt 63 110 0 13 Nov 2018
Confounding variables can degrade generalization performance of radiological deep learning models J. Zech Marcus A. Badgeley Manway Liu A. Costa J. Titano Eric K. Oermann OOD 85 1,176 0 02 Jul 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 79 528 0 21 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 95 1,857 0 31 May 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 131 437 0 28 May 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 129 3,961 0 06 Feb 2018
What do we need to build explainable AI systems for the medical domain? Andreas Holzinger Chris Biemann C. Pattichis D. Kell 71 691 0 28 Dec 2017
Inverse Classification for Comparison-based Interpretability in Machine Learning Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 123 101 0 22 Dec 2017
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes Nikhil Garg L. Schiebinger Dan Jurafsky James Zou AI4TS 69 965 0 22 Nov 2017
Building machines that adapt and compute like brains Brenden M. Lake J. Tenenbaum AI4CE FedML NAI AILaw 320 887 0 11 Nov 2017