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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 / 4,973 papers shown

Title
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models Eric Wallace Jens Tuyls Junlin Wang Sanjay Subramanian Matt Gardner Sameer Singh MILM 85 138 0 19 Sep 2019
Representation Learning for Electronic Health Records W. Weng Peter Szolovits 76 19 0 19 Sep 2019
InterpretML: A Unified Framework for Machine Learning Interpretability Harsha Nori Samuel Jenkins Paul Koch R. Caruana AI4CE 166 490 0 19 Sep 2019
Slices of Attention in Asynchronous Video Job Interviews Léo Hemamou G. Felhi Jean-Claude Martin Chloé Clavel 37 21 0 19 Sep 2019
Large-scale representation learning from visually grounded untranscribed speech Gabriel Ilharco Yuan Zhang Jason Baldridge SSL 82 61 0 19 Sep 2019
Semantically Interpretable Activation Maps: what-where-how explanations within CNNs Diego Marcos Sylvain Lobry D. Tuia FAtt MILM 59 28 0 18 Sep 2019
The Explanation Game: Explaining Machine Learning Models Using Shapley Values Luke Merrick Ankur Taly FAtt TDI 47 33 0 17 Sep 2019
X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust Arjun Reddy Akula Changsong Liu Sari Saba-Sadiya Hongjing Lu S. Todorovic J. Chai Song-Chun Zhu 62 18 0 15 Sep 2019
Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis and Decision Making Devin Taylor Simeon E. Spasov Pietro Lio 31 5 0 13 Sep 2019
Shapley Interpretation and Activation in Neural Networks Yadong Li Xin Cui TDI FAtt LLMSV 37 3 0 13 Sep 2019
New Perspective of Interpretability of Deep Neural Networks Masanari Kimura Masayuki Tanaka AAML FAtt FaML AI4CE 35 6 0 12 Sep 2019
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency Kacper Sokol Raúl Santos-Rodríguez Peter A. Flach 55 37 0 11 Sep 2019
Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data Xiaozhe Gu Arvind Easwaran 54 30 0 11 Sep 2019
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks Isaac Ahern Adam Noack Luis Guzman-Nateras Dejing Dou Boyang Albert Li Jun Huan FAtt 55 40 0 10 Sep 2019
Learning Fair Rule Lists Ulrich Aïvodji Julien Ferry Sébastien Gambs Marie-José Huguet Mohamed Siala FaML 61 11 0 09 Sep 2019
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey Jérôme Rony Soufiane Belharbi Jose Dolz Ismail Ben Ayed Luke McCaffrey Eric Granger 159 74 0 08 Sep 2019
Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations Liam Hiley Alun D. Preece Y. Hicks XAI 34 15 0 07 Sep 2019
Equalizing Recourse across Groups Vivek Gupta Pegah Nokhiz Chitradeep Dutta Roy Suresh Venkatasubramanian FaML 65 72 0 07 Sep 2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 76 392 0 06 Sep 2019
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning Theo Jaunet Romain Vuillemot Christian Wolf HAI 128 36 0 06 Sep 2019
Testing Deep Learning Models for Image Analysis Using Object-Relevant Metamorphic Relations Yongqiang Tian Shiqing Ma Ming Wen Yepang Liu Shing-Chi Cheung Xinming Zhang VLM 54 5 0 06 Sep 2019
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI Dakuo Wang Justin D. Weisz Michael J. Muller Parikshit Ram Werner Geyer Casey Dugan Y. Tausczik Horst Samulowitz Alexander G. Gray 228 315 0 05 Sep 2019
ALIME: Autoencoder Based Approach for Local Interpretability Sharath M. Shankaranarayana D. Runje FAtt 63 105 0 04 Sep 2019
Towards Interpretable Polyphonic Transcription with Invertible Neural Networks Rainer Kelz Gerhard Widmer 41 15 0 04 Sep 2019
Understanding Bias in Machine Learning Jindong Gu Daniela Oelke AI4CE FaML 31 22 0 02 Sep 2019
Human-grounded Evaluations of Explanation Methods for Text Classification Piyawat Lertvittayakumjorn Francesca Toni FAtt 90 67 0 29 Aug 2019
Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks Maximilian Pichler V. Boreux A. Klein M. Schleuning F. Hartig 42 101 0 26 Aug 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 115 55 0 24 Aug 2019
Fairness in Deep Learning: A Computational Perspective Mengnan Du Fan Yang Na Zou Helen Zhou FaML FedML 66 234 0 23 Aug 2019
The many Shapley values for model explanation Mukund Sundararajan A. Najmi TDI FAtt 70 644 0 22 Aug 2019
Saliency Methods for Explaining Adversarial Attacks Jindong Gu Volker Tresp FAtt AAML 71 30 0 22 Aug 2019
TabNet: Attentive Interpretable Tabular Learning Sercan O. Arik Tomas Pfister LMTD 222 1,381 0 20 Aug 2019
Fine-grained Sentiment Analysis with Faithful Attention Ruiqi Zhong Steven Shao Kathleen McKeown 103 50 0 19 Aug 2019
Fairness Issues in AI Systems that Augment Sensory Abilities Leah Findlater Steven M. Goodman Yuhang Zhao Shiri Azenkot Margot Hanley 40 25 0 16 Aug 2019
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization Laurent Risser Alberto González Sanz Quentin Vincenot Jean-Michel Loubes 91 21 0 15 Aug 2019
Visualizing Image Content to Explain Novel Image Discovery Jake H. Lee K. Wagstaff 29 3 0 14 Aug 2019
Requirements Engineering for Machine Learning: Perspectives from Data Scientists Andreas Vogelsang Markus Borg 70 164 0 13 Aug 2019
Learning Credible Deep Neural Networks with Rationale Regularization Mengnan Du Ninghao Liu Fan Yang Helen Zhou FaML 98 46 0 13 Aug 2019
Regional Tree Regularization for Interpretability in Black Box Models Mike Wu S. Parbhoo M. C. Hughes R. Kindle Leo Anthony Celi Maurizio Zazzi Volker Roth Finale Doshi-Velez 76 38 0 13 Aug 2019
A Survey of Challenges and Opportunities in Sensing and Analytics for Cardiovascular Disorders N. Hurley E. Spatz H. Krumholz R. Jafari B. Mortazavi 41 1 0 12 Aug 2019
LoRMIkA: Local rule-based model interpretability with k-optimal associations Dilini Sewwandi Rajapaksha Christoph Bergmeir Wray Buntine 90 31 0 11 Aug 2019
Neural Image Compression and Explanation Xiang Li Shihao Ji 25 10 0 09 Aug 2019
Measurable Counterfactual Local Explanations for Any Classifier Adam White Artur Garcez FAtt 70 98 0 08 Aug 2019
Investigating Decision Boundaries of Trained Neural Networks Roozbeh Yousefzadeh D. O’Leary AAML 48 22 0 07 Aug 2019
Advocacy Learning: Learning through Competition and Class-Conditional Representations Ian Fox Jenna Wiens SSL 32 2 0 07 Aug 2019
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Jörg Wagner Jan M. Köhler Tobias Gindele Leon Hetzel Thaddäus Wiedemer Sven Behnke AAML FAtt 98 122 0 07 Aug 2019
Knowledge Consistency between Neural Networks and Beyond Ruofan Liang Tianlin Li Longfei Li Jingchao Wang Quanshi Zhang 76 28 0 05 Aug 2019
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations Chanatip Saetia Ekapol Chuangsuwanich Tawunrat Chalothorn P. Vateekul 74 5 0 04 Aug 2019
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models Daniel Omeiza Skyler Speakman C. Cintas Komminist Weldemariam FAtt 72 218 0 03 Aug 2019
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems Wenbo Guo Lun Wang Masashi Sugiyama Min Du Basel Alomair 86 230 0 02 Aug 2019