Counterfactual Evaluation for Explainable AI

Counterfactual Evaluation for Explainable AI

5 September 2021

Juntao Tan

Papers citing "Counterfactual Evaluation for Explainable AI"

14 / 14 papers shown

Title
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation Qianli Wang Nils Feldhus Simon Ostermann Luis Felipe Villa-Arenas Sebastian Möller Vera Schmitt AAML 34 0 0 01 Jan 2025
SCENE: Evaluating Explainable AI Techniques Using Soft Counterfactuals Haoran Zheng Utku Pamuksuz 19 0 0 08 Aug 2024
Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk Seyma Gunonu Gizem Altun Mustafa Cavus 30 0 0 14 Jul 2024
How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors? Zachariah Carmichael Walter J. Scheirer FAtt 25 4 0 27 Oct 2023
Defending Against Authorship Identification Attacks Haining Wang 14 1 0 02 Oct 2023
Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI Ivania Donoso-Guzmán Jeroen Ooge Denis Parra K. Verbert 29 5 0 31 Jul 2023
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models Ian E. Nielsen Ravichandran Ramachandran N. Bouaynaya Hassan M. Fathallah-Shaykh Ghulam Rasool AAML FAtt 38 7 0 15 Mar 2023
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 42 18 0 31 May 2022
Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI Suzanna Sia Anton Belyy Amjad Almahairi Madian Khabsa Luke Zettlemoyer Lambert Mathias LRM 12 13 0 25 May 2022
Deconfounded Causal Collaborative Filtering Shuyuan Xu Juntao Tan Shelby Heinecke Jia Li Yongfeng Zhang CML 22 40 0 14 Oct 2021
Problem Learning: Towards the Free Will of Machines Yongfeng Zhang FaML 15 2 0 01 Sep 2021
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 24 106 0 20 Oct 2020
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 219 201 0 06 Jul 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017