Roadmap of Designing Cognitive Metrics for Explainable Artificial
Intelligence (XAI)

Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)

20 July 2021

Papers citing "Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)"

18 / 18 papers shown

Title
Quantitative Evaluations on Saliency Methods: An Experimental Study Xiao-hui Li Yuhan Shi Haoyang Li Wei Bai Yuanwei Song Caleb Chen Cao Lei Chen FAtt XAI 82 18 0 31 Dec 2020
On Controllability of AI Roman V. Yampolskiy 39 14 0 19 Jul 2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? Peter Hase Joey Tianyi Zhou FAtt 70 302 0 04 May 2020
The Pragmatic Turn in Explainable Artificial Intelligence (XAI) Andrés Páez 64 196 0 22 Feb 2020
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study Ahmed Alqaraawi M. Schuessler Philipp Weiß Enrico Costanza N. Bianchi-Berthouze AAML FAtt XAI 59 200 0 03 Feb 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 107 715 0 08 Jan 2020
Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations Andreas Holzinger André M. Carrington Heimo Muller LRM XAI ELM 66 308 0 19 Dec 2019
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI Shane T. Mueller R. Hoffman W. Clancey Abigail Emrey Gary Klein XAI 47 286 0 05 Feb 2019
Explainable artificial intelligence (XAI), the goodness criteria and the grasp-ability test Tae Wan Kim XAI 25 15 0 22 Oct 2018
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction Jianguo Zhang Ji Wang Lifang He Zhao Li Philip S. Yu 49 31 0 11 Sep 2018
xGEMs: Generating Examplars to Explain Black-Box Models Shalmali Joshi Oluwasanmi Koyejo Been Kim Joydeep Ghosh MLAU 44 40 0 22 Jun 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 189 1,834 0 30 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 236 4,249 0 22 Jun 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,517 0 11 Apr 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 154 3,685 0 10 Jun 2016
Generating Visual Explanations Lisa Anne Hendricks Zeynep Akata Marcus Rohrbach Jeff Donahue Bernt Schiele Trevor Darrell VLM FAtt 81 618 0 28 Mar 2016
Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to the iCub's answers I. Gaudiello E. Zibetti S. Lefort Mohamed Chetouani S. Ivaldi 24 184 0 13 Oct 2015
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition Jeff Donahue Yangqing Jia Oriol Vinyals Judy Hoffman Ning Zhang Eric Tzeng Trevor Darrell VLM ObjD 176 4,948 0 06 Oct 2013