"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 / 4,267 papers shown

Title
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations Fred Hohman Haekyu Park Caleb Robinson Duen Horng Chau FAtt 3DH HAI 19 214 0 04 Apr 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 25 21 0 01 Apr 2019
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation Marco Ancona Cengiz Öztireli Markus Gross FAtt TDI 27 223 0 26 Mar 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 Mar 2019
Natural Language Interaction with Explainable AI Models Arjun Reddy Akula S. Todorovic J. Chai Song-Chun Zhu 24 23 0 13 Mar 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira Lior Rokach FAtt TDI 19 86 0 06 Mar 2019
Copying Machine Learning Classifiers Irene Unceta Jordi Nin O. Pujol 14 18 0 05 Mar 2019
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning Alicja Gosiewska A. Gacek Piotr Lubon P. Biecek 20 5 0 28 Feb 2019
Deep learning in bioinformatics: introduction, application, and perspective in big data era Yu Li Chao Huang Lizhong Ding Zhongxiao Li Yijie Pan Xin Gao AI4CE 24 295 0 28 Feb 2019
Reliable Deep Grade Prediction with Uncertainty Estimation Qian Hu Huzefa Rangwala 18 39 0 26 Feb 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 17 996 0 26 Feb 2019
Saliency Learning: Teaching the Model Where to Pay Attention Reza Ghaeini Xiaoli Z. Fern Hamed Shahbazi Prasad Tadepalli FAtt XAI 29 30 0 22 Feb 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric Xing Ameet Talwalkar AAML 24 79 0 18 Feb 2019
Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability Brian Lubars Chenhao Tan 22 73 0 08 Feb 2019
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making Carrie J. Cai Emily Reif Narayan Hegde J. Hipp Been Kim ... Martin Wattenberg F. Viégas G. Corrado Martin C. Stumpe Michael Terry 43 397 0 08 Feb 2019
Fairwashing: the risk of rationalization Ulrich Aïvodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 19 142 0 28 Jan 2019
Testing Conditional Independence in Supervised Learning Algorithms David S. Watson Marvin N. Wright CML 29 52 0 28 Jan 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 39 449 0 27 Jan 2019
The autofeat Python Library for Automated Feature Engineering and Selection F. Horn R. Pack M. Rieger 15 93 0 22 Jan 2019
Explainable Failure Predictions with RNN Classifiers based on Time Series Data I. Giurgiu Anika Schumann AI4TS 11 8 0 20 Jan 2019
On Network Science and Mutual Information for Explaining Deep Neural Networks Brian Davis Umang Bhatt Kartikeya Bhardwaj R. Marculescu J. M. F. Moura FedML SSL FAtt 21 10 0 20 Jan 2019
Quantifying Interpretability and Trust in Machine Learning Systems Philipp Schmidt F. Biessmann 16 112 0 20 Jan 2019
Towards Aggregating Weighted Feature Attributions Umang Bhatt Pradeep Ravikumar José M. F. Moura FAtt TDI 6 13 0 20 Jan 2019
Visual Entailment: A Novel Task for Fine-Grained Image Understanding Ning Xie Farley Lai Derek Doran Asim Kadav CoGe 53 322 0 20 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 49 1,421 0 14 Jan 2019
Enhancing Explainability of Neural Networks through Architecture Constraints Zebin Yang Aijun Zhang Agus Sudjianto AAML 16 87 0 12 Jan 2019
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions Upol Ehsan Pradyumna Tambwekar Larry Chan Brent Harrison Mark O. Riedl 19 237 0 11 Jan 2019
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries Christian Scano Battista Biggio Giovanni Lagorio Fabio Roli A. Armando AAML 24 129 0 11 Jan 2019
Interpretable CNNs for Object Classification Quanshi Zhang Xin Eric Wang Ying Nian Wu Huilin Zhou Song-Chun Zhu 15 54 0 08 Jan 2019
Ten ways to fool the masses with machine learning F. Minhas Amina Asif Asa Ben-Hur FedML HAI 33 5 0 07 Jan 2019
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning Peter F. Schulam Suchi Saria OOD 27 103 0 02 Jan 2019
Efficient Search for Diverse Coherent Explanations Chris Russell 17 234 0 02 Jan 2019
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions Christopher J. Hazard Christopher Fusting Michael Resnick Michael Auerbach M. Meehan Valeri Korobov 14 8 0 02 Jan 2019
Explaining Aggregates for Exploratory Analytics Fotis Savva Christos Anagnostopoulos Peter Triantafillou 11 18 0 29 Dec 2018
A Multi-Objective Anytime Rule Mining System to Ease Iterative Feedback from Domain Experts T. Baum Steffen Herbold K. Schneider 11 4 0 23 Dec 2018
Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution Baptiste Broto François Bachoc M. Depecker FAtt 19 52 0 21 Dec 2018
LEAFAGE: Example-based and Feature importance-based Explanationsfor Black-box ML models Ajaya Adhikari David Tax R. Satta M. Faeth FAtt 25 11 0 21 Dec 2018
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering Quanshi Zhang Ruiming Cao Ying Nian Wu Song-Chun Zhu 10 14 0 18 Dec 2018
Explaining Neural Networks Semantically and Quantitatively Runjin Chen Hao Chen Ge Huang Jie Ren Quanshi Zhang FAtt 23 54 0 18 Dec 2018
Interactive Naming for Explaining Deep Neural Networks: A Formative Study M. Hamidi-Haines Zhongang Qi Alan Fern Fuxin Li Prasad Tadepalli FAtt HAI 19 11 0 18 Dec 2018
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation Rakshith Shetty Bernt Schiele Mario Fritz 29 83 0 17 Dec 2018
Can I trust you more? Model-Agnostic Hierarchical Explanations Michael Tsang Youbang Sun Dongxu Ren Yan Liu FAtt 16 25 0 12 Dec 2018
Skin Lesions Classification Using Convolutional Neural Networks in Clinical Images Danilo Barros Mendes Nilton Correia da Silva MedIm 22 46 0 06 Dec 2018
Understanding Individual Decisions of CNNs via Contrastive Backpropagation Jindong Gu Yinchong Yang Volker Tresp FAtt 17 94 0 05 Dec 2018
e-SNLI: Natural Language Inference with Natural Language Explanations Oana-Maria Camburu Tim Rocktaschel Thomas Lukasiewicz Phil Blunsom LRM 287 623 0 04 Dec 2018
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 30 169 0 03 Dec 2018
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems Sina Mohseni Niloofar Zarei Eric D. Ragan 31 102 0 28 Nov 2018
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration M. Cavallo Çağatay Demiralp 11 55 0 28 Nov 2018
Abduction-Based Explanations for Machine Learning Models Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt 20 219 0 26 Nov 2018
How to improve the interpretability of kernel learning Jinwei Zhao Qizhou Wang Yufei Wang Yu Liu Zhenghao Shi Xinhong Hei FAtt 19 0 0 21 Nov 2018