TED: Teaching AI to Explain its Decisions

TED: Teaching AI to Explain its Decisions

12 November 2018

Murray Campbell

Amit Dhurandhar

Aleksandra Mojsilović

Karthikeyan N. Ramamurthy

Kush R. Varshney

Papers citing "TED: Teaching AI to Explain its Decisions"

19 / 19 papers shown

Title
Explainable AI: Definition and attributes of a good explanation for health AI E. Kyrimi S. McLachlan Jared M Wohlgemut Zane B Perkins David A. Lagnado W. Marsh the ExAIDSS Expert Group XAI 36 1 0 09 Sep 2024
Are Data-driven Explanations Robust against Out-of-distribution Data? Tang Li Fengchun Qiao Mengmeng Ma Xiangkai Peng OODD OOD 43 10 0 29 Mar 2023
Towards Prototype-Based Self-Explainable Graph Neural Network Enyan Dai Suhang Wang 33 12 0 05 Oct 2022
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 40 11 0 13 May 2022
Study of Feature Importance for Quantum Machine Learning Models Aaron Baughman Kavitha Yogaraj Rajat Hebbar S. Ghosh R. Haq Yoshika Chhabra 21 12 0 18 Feb 2022
Explanatory Learning: Beyond Empiricism in Neural Networks Antonio Norelli Giorgio Mariani Luca Moschella Andrea Santilli Giambattista Parascandolo Simone Melzi Emanuele Rodolà 16 2 0 25 Jan 2022
Image Classification with Consistent Supporting Evidence Peiqi Wang Ruizhi Liao Daniel Moyer Seth Berkowitz Steven Horng Polina Golland 49 2 0 13 Nov 2021
Towards Self-Explainable Graph Neural Network Enyan Dai Suhang Wang 36 84 0 26 Aug 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 34 140 0 17 May 2021
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction Aniruddh Raghu John Guttag K. Young E. Pomerantsev Adrian Dalca Collin M. Stultz 13 9 0 04 Mar 2021
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research Markus Langer Daniel Oster Timo Speith Holger Hermanns Lena Kästner Eva Schmidt Andreas Sesing Kevin Baum XAI 68 415 0 15 Feb 2021
Expanding Explainability: Towards Social Transparency in AI systems Upol Ehsan Q. V. Liao Michael J. Muller Mark O. Riedl Justin D. Weisz 43 394 0 12 Jan 2021
Why model why? Assessing the strengths and limitations of LIME Jurgen Dieber S. Kirrane FAtt 26 97 0 30 Nov 2020
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction Esther Puyol-Antón Chong Chen J. Clough B. Ruijsink B. Sidhu ... M. Elliott Vishal S. Mehta Daniel Rueckert C. Rinaldi A. King 21 32 0 24 Jun 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 15 17 0 05 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 38 218 0 01 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 52 371 0 30 Apr 2020
xCos: An Explainable Cosine Metric for Face Verification Task Yu-sheng Lin Zhe-Yu Liu Yu-An Chen Yu-Siang Wang Ya-Liang Chang Winston H. Hsu CVBM 33 46 0 11 Mar 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,242 0 24 Jun 2017