Features of Explainability: How users understand counterfactual and
causal explanations for categorical and continuous features in XAI

Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI

21 April 2022

Papers citing "Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI"

13 / 13 papers shown

Title
Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments M. Domnich Julius Valja Rasmus Moorits Veski Giacomo Magnifico Kadi Tulver Eduard Barbu Raul Vicente LRM ELM 40 2 0 28 Oct 2024
Cultural Bias in Explainable AI Research: A Systematic Analysis Uwe Peters Mary Carman 21 10 0 28 Feb 2024
The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes Eoin M. Kenny Weipeng Huang 24 9 0 29 Oct 2023
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems Yiming Li Daling Wang Wenfang Wu Shi Feng Yifei Zhang CML 32 1 0 28 Sep 2023
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI Ulrike Kuhl André Artelt Barbara Hammer 11 3 0 13 Jun 2023
Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals Greta Warren Markt. Keane Christophe Guéret Eoin Delaney 21 13 0 16 Mar 2023
Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise Courtney Ford Markt. Keane 14 11 0 19 Dec 2022
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ Eoin Delaney A. Pakrashi Derek Greene Markt. Keane 21 15 0 16 Dec 2022
Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that? Marko Tešić U. Hahn CML 9 5 0 12 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 27 17 0 06 May 2022
Situated Conditional Reasoning Giovanni Casini T. Meyer I. Varzinczak 19 2 0 03 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
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,681 0 28 Feb 2017