"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 / 4,266 papers shown

Title
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making Michael Veale Max Van Kleek Reuben Binns 25 409 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 38 241 0 02 Feb 2018
Visual Interpretability for Deep Learning: a Survey Quanshi Zhang Song-Chun Zhu FaML HAI 17 809 0 02 Feb 2018
Interpretable Deep Convolutional Neural Networks via Meta-learning Xuan Liu Xiaoguang Wang Stan Matwin FaML 27 6 0 02 Feb 2018
A Comparative Study of Rule Extraction for Recurrent Neural Networks Qinglong Wang Kaixuan Zhang Alexander Ororbia Masashi Sugiyama Xue Liu C. Lee Giles 18 11 0 16 Jan 2018
What do we need to build explainable AI systems for the medical domain? Andreas Holzinger Chris Biemann C. Pattichis D. Kell 28 680 0 28 Dec 2017
The Robust Manifold Defense: Adversarial Training using Generative Models A. Jalal Andrew Ilyas C. Daskalakis A. Dimakis AAML 31 174 0 26 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 19 91 0 18 Dec 2017
A trans-disciplinary review of deep learning research for water resources scientists Chaopeng Shen AI4CE 33 682 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 11 372 0 02 Dec 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 77 1,800 0 30 Nov 2017
Contextual Outlier Interpretation Ninghao Liu DongHwa Shin Xia Hu 17 72 0 28 Nov 2017
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 37 676 0 26 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 28 281 0 16 Nov 2017
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis Rushil Anirudh Jayaraman J. Thiagarajan R. Sridhar T. Bremer FAtt AAML 23 12 0 15 Nov 2017
Dynamic Analysis of Executables to Detect and Characterize Malware Michael R. Smith J. Ingram Christopher C. Lamb T. Draelos J. Doak J. Aimone C. James 23 13 0 10 Nov 2017
Visualizing and Understanding Atari Agents S. Greydanus Anurag Koul Jonathan Dodge Alan Fern FAtt 37 342 0 31 Oct 2017
Do Convolutional Neural Networks Learn Class Hierarchy? B. Alsallakh Amin Jourabloo Mao Ye Xiaoming Liu Liu Ren 42 210 0 17 Oct 2017
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward Ramnath Kumar Andrew W. Wicker Matt Swann AAML 21 43 0 20 Sep 2017
Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization Jaedeok Kim Ji-Hoon Seo FAtt 18 8 0 18 Sep 2017
Learning the PE Header, Malware Detection with Minimal Domain Knowledge Edward Raff Jared Sylvester Charles K. Nicholas 28 118 0 05 Sep 2017
Understanding and Comparing Deep Neural Networks for Age and Gender Classification Sebastian Lapuschkin Alexander Binder K. Müller Wojciech Samek CVBM 37 135 0 25 Aug 2017
Machine learning for neural decoding Joshua I. Glaser Ari S. Benjamin Raeed H. Chowdhury M. Perich L. Miller Konrad Paul Kording 33 242 0 02 Aug 2017
Using Program Induction to Interpret Transition System Dynamics Svetlin Penkov S. Ramamoorthy AI4CE 35 11 0 26 Jul 2017
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? Lin Chen Moran Feldman Amin Karbasi 21 47 0 13 Jul 2017
A Formal Framework to Characterize Interpretability of Procedures Amit Dhurandhar Vijay Iyengar Ronny Luss Karthikeyan Shanmugam 15 19 0 12 Jul 2017
Efficient Data Representation by Selecting Prototypes with Importance Weights Karthik S. Gurumoorthy Amit Dhurandhar Guillermo Cecchi Charu Aggarwal 26 22 0 05 Jul 2017
Interpretability via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 26 129 0 29 Jun 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 21 4,203 0 22 Jun 2017
MAGIX: Model Agnostic Globally Interpretable Explanations Nikaash Puri Piyush B. Gupta Pratiksha Agarwal Sukriti Verma Balaji Krishnamurthy FAtt 29 41 0 22 Jun 2017
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking Gabriele Tolomei Fabrizio Silvestri Andrew Haines M. Lalmas 9 203 0 20 Jun 2017
Contextual Explanation Networks Maruan Al-Shedivat Kumar Avinava Dubey Eric Xing CML 37 82 0 29 May 2017
Interpreting Blackbox Models via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 35 170 0 23 May 2017
Induction of Interpretable Possibilistic Logic Theories from Relational Data Ondrej Kuzelka Jesse Davis Steven Schockaert NAI 33 12 0 19 May 2017
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations Josua Krause Aritra Dasgupta Jordan Swartz Yindalon Aphinyanagphongs E. Bertini FAtt 19 95 0 04 May 2017
Translating Neuralese Jacob Andreas Anca Dragan Dan Klein 31 58 0 23 Apr 2017
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models Minsuk Kahng Pierre Yves Andrews Aditya Kalro Duen Horng Chau HAI 23 322 0 06 Apr 2017
It Takes Two to Tango: Towards Theory of AI's Mind Arjun Chandrasekaran Deshraj Yadav Prithvijit Chattopadhyay Viraj Prabhu Devi Parikh 38 54 0 03 Apr 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 43 583 0 10 Mar 2017
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly Ethan R. Elenberg A. Dimakis Moran Feldman Amin Karbasi 19 89 0 08 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 45 5,865 0 04 Mar 2017
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations Upol Ehsan Brent Harrison Larry Chan Mark O. Riedl 25 217 0 25 Feb 2017
EVE: Explainable Vector Based Embedding Technique Using Wikipedia M. A. Qureshi Derek Greene 25 33 0 22 Feb 2017
Deep Reinforcement Learning: An Overview Yuxi Li OffRL VLM 104 1,503 0 25 Jan 2017
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning Rock Stevens H. Aggarwal Himani Arora Sanghyun Hong M. Hicks Chetan Arora SILM AAML 11 18 0 17 Jan 2017
Identifying Best Interventions through Online Importance Sampling Rajat Sen Karthikeyan Shanmugam A. Dimakis Sanjay Shakkottai 28 72 0 10 Jan 2017
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets L. Micallef Iiris Sundin Pekka Marttinen Muhammad Ammad-ud-din Tomi Peltola Marta Soare Giulio Jacucci Samuel Kaski 11 28 0 07 Dec 2016
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering Yash Goyal Tejas Khot D. Summers-Stay Dhruv Batra Devi Parikh CoGe 143 3,130 0 02 Dec 2016
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation Wojciech Samek G. Montavon Alexander Binder Sebastian Lapuschkin K. Müller FAtt AI4CE 52 48 0 24 Nov 2016