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"

16 / 16 papers shown

Title
Investigating the Duality of Interpretability and Explainability in Machine Learning Moncef Garouani Josiane Mothe Ayah Barhrhouj Julien Aligon AAML 42 2 0 27 Mar 2025
TagGAN: A Generative Model for Data Tagging Muhammad Nawaz Basma Nasir Tehseen Zia Zawar Hussain Catarina Moreira GAN MedIm 42 0 0 25 Feb 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 27 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 34 78 0 25 Jan 2024
Local and Regional Counterfactual Rules: Summarized and Robust Recourses Salim I. Amoukou Nicolas Brunel 26 0 0 29 Sep 2022
Measuring Interventional Robustness in Reinforcement Learning Katherine Avery Jack Kenney Pracheta Amaranath Erica Cai David D. Jensen 21 0 0 19 Sep 2022
Causal Machine Learning for Healthcare and Precision Medicine Pedro Sanchez J. Voisey Tian Xia Hannah I. Watson Alison Q. OÑeil Sotirios A. Tsaftaris OOD CML 42 109 0 23 May 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 32 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 27 22 0 21 Apr 2022
Pitfalls of Explainable ML: An Industry Perspective Sahil Verma Aditya Lahiri John P. Dickerson Su-In Lee XAI 16 9 0 14 Jun 2021
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making Md. Naimul Hoque Klaus Mueller CML 51 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 36 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 57 93 0 05 Mar 2020
Building machines that adapt and compute like brains Brenden Lake J. Tenenbaum AI4CE FedML NAI AILaw 254 890 0 11 Nov 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017