Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that?

12 May 2022

Papers citing "Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that?"

18 / 18 papers shown

Title
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 53 22 0 21 Apr 2022
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals Sainyam Galhotra Romila Pradhan Babak Salimi CML 62 107 0 22 Mar 2021
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 44 101 0 10 Nov 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 62 172 0 08 Oct 2020
FACE: Feasible and Actionable Counterfactual Explanations Rafael Poyiadzi Kacper Sokol Raúl Santos-Rodríguez T. D. Bie Peter A. Flach 68 369 0 20 Sep 2019
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting Ana Lucic H. Haned Maarten de Rijke 59 63 0 17 Jul 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 91 321 0 27 May 2019
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 99 242 0 02 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 107 2,352 0 01 Nov 2017
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models Wojciech Samek Thomas Wiegand K. Müller XAI VLM 72 1,189 0 28 Aug 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 285 2,264 0 24 Jun 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 242 4,262 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 203 2,885 0 14 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 399 3,787 0 28 Feb 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
Fitting Linear Mixed-Effects Models using lme4 D. Bates M. Machler B. Bolker Steven C. Walker KELM 60 71,814 0 23 Jun 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,292 0 20 Dec 2013