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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

16 February 2016

Papers citing ""Why Should I Trust You?": Explaining the Predictions of Any Classifier"

50 / 4,966 papers shown

Title
Visual Psychophysics for Making Face Recognition Algorithms More Explainable Brandon RichardWebster So Yon Kwon Christopher Clarizio Samuel E. Anthony Walter J. Scheirer CVBM 60 41 0 19 Mar 2018
Towards Explanation of DNN-based Prediction with Guided Feature Inversion Mengnan Du Ninghao Liu Qingquan Song Helen Zhou FAtt 97 127 0 19 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 156 508 0 13 Mar 2018
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge Kai Xu Dae Hoon Park Chang Yi Charles Sutton HAI FAtt 67 26 0 11 Mar 2018
Explaining Black-box Android Malware Detection Marco Melis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 49 44 0 09 Mar 2018
The Challenge of Crafting Intelligible Intelligence Daniel S. Weld Gagan Bansal 58 244 0 09 Mar 2018
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification Chengliang Yang Anand Rangarajan Sanjay Ranka FAtt 63 142 0 07 Mar 2018
Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots Javier Chiyah-Garcia D. A. Robb Xingkun Liu A. Laskov P. Patrón H. Hastie 71 25 0 06 Mar 2018
On Cognitive Preferences and the Plausibility of Rule-based Models Johannes Furnkranz Tomáš Kliegr Heiko Paulheim LRM 73 71 0 04 Mar 2018
Improved Explainability of Capsule Networks: Relevance Path by Agreement Atefeh Shahroudnejad Arash Mohammadi Konstantinos N. Plataniotis AAML MedIm 50 62 0 27 Feb 2018
Interpreting Complex Regression Models Noa Avigdor-Elgrabli Alex Libov M. Viderman R. Wolff 37 1 0 26 Feb 2018
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions Hai Ye Xin Jiang Zhunchen Luo Wen-Han Chao 80 132 0 23 Feb 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan MLT FAtt 184 576 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 140 592 0 21 Feb 2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections Xin Zhang Armando Solar-Lezama Rishabh Singh FAtt 115 63 0 21 Feb 2018
Teaching Categories to Human Learners with Visual Explanations Oisin Mac Aodha Shihan Su Yuxin Chen Pietro Perona Yisong Yue 135 71 0 20 Feb 2018
Finding Influential Training Samples for Gradient Boosted Decision Trees B. Sharchilev Yury Ustinovsky P. Serdyukov Maarten de Rijke TDI 74 57 0 19 Feb 2018
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution Lingyang Chu X. Hu Juhua Hu Lanjun Wang J. Pei 62 100 0 17 Feb 2018
Explainable Prediction of Medical Codes from Clinical Text J. Mullenbach Sarah Wiegreffe J. Duke Jimeng Sun Jacob Eisenstein FAtt 95 576 0 15 Feb 2018
Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute Tong Wang 50 8 0 12 Feb 2018
Evaluating Compositionality in Sentence Embeddings Ishita Dasgupta Demi Guo Andreas Stuhlmuller S. Gershman Noah D. Goodman CoGe 98 121 0 12 Feb 2018
Consistent Individualized Feature Attribution for Tree Ensembles Scott M. Lundberg G. Erion Su-In Lee FAtt TDI 86 1,408 0 12 Feb 2018
Influence-Directed Explanations for Deep Convolutional Networks Klas Leino S. Sen Anupam Datta Matt Fredrikson Linyi Li TDI FAtt 113 75 0 11 Feb 2018
Global Model Interpretation via Recursive Partitioning Chengliang Yang Anand Rangarajan Sanjay Ranka FAtt 62 80 0 11 Feb 2018
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference Peter Alexander Jansen Elizabeth Wainwright Steven Marmorstein Clayton T. Morrison 89 123 0 08 Feb 2018
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks Patrick Schwab Djordje Miladinovic W. Karlen CML 99 57 0 06 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 179 3,996 0 06 Feb 2018
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making Michael Veale Max Van Kleek Reuben Binns 71 424 0 03 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 110 243 0 02 Feb 2018
Visual Interpretability for Deep Learning: a Survey Quanshi Zhang Song-Chun Zhu FaML HAI 179 822 0 02 Feb 2018
Interpretable Deep Convolutional Neural Networks via Meta-learning Xuan Liu Xiaoguang Wang Stan Matwin FaML 154 38 0 02 Feb 2018
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations M. Harradon Jeff Druce Brian E. Ruttenberg BDL CML 53 82 0 02 Feb 2018
Interpreting CNNs via Decision Trees Quanshi Zhang Yu Yang Ying Nian Wu Song-Chun Zhu FAtt 88 323 0 01 Feb 2018
Ít's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions Reuben Binns Max Van Kleek Michael Veale Ulrik Lyngs Jun Zhao N. Shadbolt FaML 77 546 0 31 Jan 2018
The Intriguing Properties of Model Explanations Maruan Al-Shedivat Kumar Avinava Dubey Eric Xing FAtt 41 7 0 30 Jan 2018
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers Fred Hohman Minsuk Kahng Robert S. Pienta Duen Horng Chau OOD HAI 101 541 0 21 Jan 2018
Evaluating neural network explanation methods using hybrid documents and morphological agreement Nina Pörner Benjamin Roth Hinrich Schütze 60 9 0 19 Jan 2018
A Comparative Study of Rule Extraction for Recurrent Neural Networks Qinglong Wang Kaixuan Zhang Alexander Ororbia Masashi Sugiyama Xue Liu C. Lee Giles 80 11 0 16 Jan 2018
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning Sina Mohseni Jeremy E. Block Eric D. Ragan FAtt XAI 88 61 0 16 Jan 2018
Deep Learning: A Critical Appraisal G. Marcus HAI VLM 149 1,043 0 02 Jan 2018
What do we need to build explainable AI systems for the medical domain? Andreas Holzinger Chris Biemann C. Pattichis D. Kell 79 694 0 28 Dec 2017
The Robust Manifold Defense: Adversarial Training using Generative Models A. Jalal Andrew Ilyas C. Daskalakis A. Dimakis AAML 109 174 0 26 Dec 2017
Dropout Feature Ranking for Deep Learning Models C. Chang Ladislav Rampášek Anna Goldenberg OOD 71 50 0 22 Dec 2017
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology Andreas Holzinger Bernd Malle Peter Kieseberg P. Roth Heimo Muller Robert Reihs K. Zatloukal 43 91 0 18 Dec 2017
A trans-disciplinary review of deep learning research for water resources scientists Chaopeng Shen AI4CE 226 699 0 06 Dec 2017
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties Garrett B. Goh Nathan Oken Hodas Charles Siegel Abhinav Vishnu 69 144 0 06 Dec 2017
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences Tim Miller Piers Howe L. Sonenberg AI4TS SyDa 86 374 0 02 Dec 2017
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases Pierre Stock Moustapha Cissé FaML 92 46 0 30 Nov 2017
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 270 1,848 0 30 Nov 2017
Contextual Outlier Interpretation Ninghao Liu DongHwa Shin Helen Zhou 60 72 0 28 Nov 2017