Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise

19 December 2022

Papers citing "Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise"

8 / 8 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 36 22 0 21 Apr 2022
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques Mark T. Keane Eoin M. Kenny Eoin Delaney Barry Smyth CML 43 146 0 26 Feb 2021
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems Mahsan Nourani J. King Eric D. Ragan 51 99 0 20 Aug 2020
An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition Sanghyeon An Min Jun Lee Sanglee Park H. Yang Jungmin So 37 79 0 12 Aug 2020
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 133 679 0 26 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 405 21,459 0 22 May 2017
Improving Human-Machine Cooperative Visual Search With Soft Highlighting R. T. Kneusel Michael C. Mozer 42 26 0 24 Dec 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 478 16,765 0 16 Feb 2016