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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 / 4,970 papers shown

Title
Assessing the Local Interpretability of Machine Learning Models Dylan Slack Sorelle A. Friedler C. Scheidegger Chitradeep Dutta Roy FAtt 60 71 0 09 Feb 2019
Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability Brian Lubars Chenhao Tan 44 76 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 126 405 0 08 Feb 2019
CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks Xinrui Cui Dan Wang F. I. Z. Jane Wang FAtt BDL 36 12 0 07 Feb 2019
Global Explanations of Neural Networks: Mapping the Landscape of Predictions Mark Ibrahim Melissa Louie C. Modarres John Paisley FAtt 90 119 0 06 Feb 2019
Neural Network Attributions: A Causal Perspective Aditya Chattopadhyay Piyushi Manupriya Anirban Sarkar V. Balasubramanian CML 91 146 0 06 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 123 205 0 06 Feb 2019
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI Shane T. Mueller R. Hoffman W. Clancey Abigail Emrey Gary Klein XAI 76 285 0 05 Feb 2019
Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions Alexandros Stergiou G. Kapidis Grigorios Kalliatakis C. Chrysoulas R. Veltkamp R. Poppe FAtt 67 47 0 04 Feb 2019
Fairwashing: the risk of rationalization Ulrich Aïvodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 64 148 0 28 Jan 2019
Testing Conditional Independence in Supervised Learning Algorithms David S. Watson Marvin N. Wright CML 98 53 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 110 456 0 27 Jan 2019
The autofeat Python Library for Automated Feature Engineering and Selection F. Horn R. Pack M. Rieger 123 98 0 22 Jan 2019
Explainable Failure Predictions with RNN Classifiers based on Time Series Data I. Giurgiu Anika Schumann AI4TS 20 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 55 10 0 20 Jan 2019
Quantifying Interpretability and Trust in Machine Learning Systems Philipp Schmidt F. Biessmann 56 115 0 20 Jan 2019
Towards Aggregating Weighted Feature Attributions Umang Bhatt Pradeep Ravikumar José M. F. Moura FAtt TDI 34 13 0 20 Jan 2019
Visual Entailment: A Novel Task for Fine-Grained Image Understanding Ning Xie Farley Lai Derek Doran Asim Kadav CoGe 127 327 0 20 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 211 1,456 0 14 Jan 2019
Enhancing Explainability of Neural Networks through Architecture Constraints Zebin Yang Aijun Zhang Agus Sudjianto AAML 52 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 111 245 0 11 Jan 2019
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries Christian Scano Battista Biggio Giovanni Lagorio Fabio Roli A. Armando AAML 80 131 0 11 Jan 2019
Interpretable CNNs for Object Classification Quanshi Zhang Xin Eric Wang Ying Nian Wu Huilin Zhou Song-Chun Zhu 61 54 0 08 Jan 2019
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks Zenan Ling Haotian Ma Yu Yang Robert C. Qiu Song-Chun Zhu Quanshi Zhang MILM 33 3 0 08 Jan 2019
Ten ways to fool the masses with machine learning F. Minhas Amina Asif Asa Ben-Hur FedML HAI 68 5 0 07 Jan 2019
Personalized explanation in machine learning: A conceptualization J. Schneider J. Handali XAI FAtt 77 17 0 03 Jan 2019
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning Peter F. Schulam Suchi Saria OOD 101 104 0 02 Jan 2019
Efficient Search for Diverse Coherent Explanations Chris Russell 80 241 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 39 8 0 02 Jan 2019
Explaining Aggregates for Exploratory Analytics Fotis Savva Christos Anagnostopoulos Peter Triantafillou 53 18 0 29 Dec 2018
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation Xuan Liu Xiaoguang Wang Stan Matwin HAI 65 101 0 28 Dec 2018
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation Hiroshi Fukui Tsubasa Hirakawa Takayoshi Yamashita H. Fujiyoshi XAI FAtt 88 409 0 25 Dec 2018
A Multi-Objective Anytime Rule Mining System to Ease Iterative Feedback from Domain Experts T. Baum Steffen Herbold K. Schneider 18 4 0 23 Dec 2018
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness Sebastian J. Vollmer Bilal A. Mateen G. Bohner Franz J. Király Rayid Ghani ... Karel G. M. Moons Gary S. Collins J. Ioannidis Chris Holmes H. Hemingway 124 39 0 21 Dec 2018
Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution Baptiste Broto François Bachoc M. Depecker FAtt 61 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 111 11 0 21 Dec 2018
Deep Transfer Learning for Static Malware Classification Li-Wei Chen 53 47 0 18 Dec 2018
Explanatory Graphs for CNNs Quanshi Zhang Xin Eric Wang Ruiming Cao Ying Nian Wu Feng Shi Song-Chun Zhu FAtt GNN 36 3 0 18 Dec 2018
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering Quanshi Zhang Ruiming Cao Ying Nian Wu Song-Chun Zhu 55 14 0 18 Dec 2018
Explaining Neural Networks Semantically and Quantitatively Runjin Chen Hao Chen Ge Huang Jie Ren Quanshi Zhang FAtt 62 56 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 50 11 0 18 Dec 2018
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan Marta Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 127 51 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 81 83 0 17 Dec 2018
Can I trust you more? Model-Agnostic Hierarchical Explanations Michael Tsang Youbang Sun Dongxu Ren Yan Liu FAtt 53 26 0 12 Dec 2018
Skin Lesions Classification Using Convolutional Neural Networks in Clinical Images Danilo Barros Mendes Nilton Correia da Silva MedIm 24 46 0 06 Dec 2018
Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems Barbara Rychalska Dominika Basaj P. Biecek OOD AAML 45 5 0 05 Dec 2018
Understanding Individual Decisions of CNNs via Contrastive Backpropagation Jindong Gu Yinchong Yang Volker Tresp FAtt 79 98 0 05 Dec 2018
e-SNLI: Natural Language Inference with Natural Language Explanations Oana-Maria Camburu Tim Rocktaschel Thomas Lukasiewicz Phil Blunsom LRM 439 642 0 04 Dec 2018
Sensitivity based Neural Networks Explanations Enguerrand Horel Virgile Mison T. Xiong K. Giesecke L. Mangu AAML XAI FAtt 70 19 0 03 Dec 2018
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 138 173 0 03 Dec 2018