A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

28 November 2018

Papers citing "A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems"

19 / 19 papers shown

Title
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When? M. Rubaiyat Hossain Mondal Prajoy Podder 23 56 0 10 Apr 2023
Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts Ashley Suh G. Appleby Erik W. Anderson Luca A. Finelli Remco Chang Dylan Cashman 32 8 0 11 May 2022
Explainable Misinformation Detection Across Multiple Social Media Platforms Gargi Joshi Ananya Srivastava Bhargav D. Yagnik Md Musleh Uddin Hasan Zainuddin Saiyed Lubna A Gabralla Ajith Abraham Rahee Walambe K. Kotecha 31 17 0 20 Mar 2022
Exploring The Role of Local and Global Explanations in Recommender Systems Marissa Radensky Doug Downey Kyle Lo Z. Popović Daniel S. Weld University of Washington LRM 13 20 0 27 Sep 2021
Some Critical and Ethical Perspectives on the Empirical Turn of AI Interpretability Jean-Marie John-Mathews 45 33 0 20 Sep 2021
Temporal Dependencies in Feature Importance for Time Series Predictions Kin Kwan Leung Clayton Rooke Jonathan Smith S. Zuberi M. Volkovs OOD AI4TS 28 24 0 29 Jul 2021
Evaluating the Correctness of Explainable AI Algorithms for Classification Orcun Yalcin Xiuyi Fan Siyuan Liu XAI FAtt 16 15 0 20 May 2021
Dissonance Between Human and Machine Understanding Zijian Zhang Jaspreet Singh U. Gadiraju Avishek Anand 51 74 0 18 Jan 2021
Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy Donald R. Honeycutt Mahsan Nourani Eric D. Ragan HAI 16 61 0 28 Aug 2020
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems Mahsan Nourani J. King Eric D. Ragan 19 98 0 20 Aug 2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks Arash Rahnama A.-Yu Tseng FAtt AAML FaML 17 5 0 20 May 2020
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition Mahsan Nourani Chiradeep Roy Tahrima Rahman Eric D. Ragan Nicholas Ruozzi Vibhav Gogate AAML 12 17 0 05 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 38 370 0 30 Apr 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 52 702 0 08 Jan 2020
An explanation method for Siamese neural networks Lev V. Utkin M. Kovalev E. Kasimov 14 14 0 18 Nov 2019
Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics Noëlie Cherrier Jean-Philippe Poli Maxime Defurne F. Sabatié AI4CE 15 13 0 17 Aug 2019
Leveraging Latent Features for Local Explanations Ronny Luss Pin-Yu Chen Amit Dhurandhar P. Sattigeri Yunfeng Zhang Karthikeyan Shanmugam Chun-Chen Tu FAtt 43 37 0 29 May 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,087 0 24 Oct 2016