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

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

7 March 2021

Catarina Moreira

Joaquim A. Jorge

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

12 / 12 papers shown

Title
Investigating the Duality of Interpretability and Explainability in Machine Learning Moncef Garouani Josiane Mothe Ayah Barhrhouj Julien Aligon AAML 34 2 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 24 1 0 30 Sep 2024
Black-Box Access is Insufficient for Rigorous AI Audits Stephen Casper Carson Ezell Charlotte Siegmann Noam Kolt Taylor Lynn Curtis ... Michael Gerovitch David Bau Max Tegmark David M. Krueger Dylan Hadfield-Menell AAML 13 76 0 25 Jan 2024
Local and Regional Counterfactual Rules: Summarized and Robust Recourses Salim I. Amoukou Nicolas Brunel 18 0 0 29 Sep 2022
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning Ulrike Kuhl André Artelt Barbara Hammer 27 17 0 06 May 2022
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI Greta Warren Mark T. Keane R. Byrne CML 20 22 0 21 Apr 2022
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making Md. Naimul Hoque Klaus Mueller CML 46 30 0 03 Jan 2021
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models Catarina Moreira Yu-Liang Chou M. Velmurugan Chun Ouyang Renuka Sindhgatta P. Bruza 31 57 0 21 Jul 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 20 41 0 06 Mar 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 49 93 0 05 Mar 2020
Building machines that adapt and compute like brains Brenden Lake J. Tenenbaum AI4CE FedML NAI AILaw 243 890 0 11 Nov 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017