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Personalized explanation in machine learning: A conceptualization

3 January 2019

Papers citing "Personalized explanation in machine learning: A conceptualization"

21 / 21 papers shown

Title
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 152 1,972 0 08 Oct 2018
Human-in-the-Loop Interpretability Prior Isaac Lage A. Ross Been Kim S. Gershman Finale Doshi-Velez 89 121 0 29 May 2018
Explainable Recommendation: A Survey and New Perspectives Yongfeng Zhang Xu Chen XAI LRM 124 879 0 30 Apr 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 111 219 0 20 Mar 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 106 701 0 21 Feb 2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 129 592 0 21 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 150 3,989 0 06 Feb 2018
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 110 244 0 02 Feb 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 252 1,849 0 30 Nov 2017
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu M. C. Hughes S. Parbhoo Maurizio Zazzi Volker Roth Finale Doshi-Velez AI4CE 132 283 0 16 Nov 2017
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions Oscar Li Hao Liu Chaofan Chen Cynthia Rudin 193 593 0 13 Oct 2017
Interpretable Convolutional Neural Networks Quanshi Zhang Ying Nian Wu Song-Chun Zhu FAtt 77 784 0 02 Oct 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 259 4,287 0 22 Jun 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 136 592 0 10 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 420 3,824 0 28 Feb 2017
Wide & Deep Learning for Recommender Systems Heng-Tze Cheng L. Koc Jeremiah Harmsen T. Shaked Tushar Chandra ... Zakaria Haque Lichan Hong Vihan Jain Xiaobing Liu Hemal Shah HAI VLM 200 3,673 0 24 Jun 2016
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions? Abhishek Das Harsh Agrawal C. L. Zitnick Devi Parikh Dhruv Batra 109 467 0 11 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,716 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 176 3,275 0 05 Dec 2014
Learning from Sparse Data by Exploiting Monotonicity Constraints Eric Altendorf Angelo C. Restificar Thomas G. Dietterich 82 118 0 04 Jul 2012