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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 / 4,971 papers shown

Title
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems Muhammad Rehman Zafar N. Khan FAtt 124 159 0 24 Jun 2019
Generating Counterfactual and Contrastive Explanations using SHAP Shubham Rathi 77 57 0 21 Jun 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 98 756 0 19 Jun 2019
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks R. Confalonieri Tillman Weyde Tarek R. Besold Fermín Moscoso del Prado Martín 58 24 0 19 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 108 120 0 19 Jun 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 88 335 0 19 Jun 2019
VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks Sefi Akerman Edan Habler A. Shabtai 82 18 0 19 Jun 2019
From Clustering to Cluster Explanations via Neural Networks Jacob R. Kauffmann Malte Esders Lukas Ruff G. Montavon Wojciech Samek K. Müller 79 72 0 18 Jun 2019
Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution Zicun Cong Lingyang Chu Lanjun Wang X. Hu J. Pei 420 5 0 17 Jun 2019
ASAC: Active Sensing using Actor-Critic models Jinsung Yoon James Jordon M. Schaar CML 59 16 0 16 Jun 2019
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning Marko Vasic Andrija Petrović Kaiyuan Wang Mladen Nikolic Rishabh Singh S. Khurshid OffRL MoE 96 25 0 16 Jun 2019
Yoga-Veganism: Correlation Mining of Twitter Health Data Tunazzina Islam 33 23 0 15 Jun 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 74 363 0 11 Jun 2019
Toward Best Practices for Explainable B2B Machine Learning Kit Kuksenok 13 0 0 11 Jun 2019
Extracting Interpretable Concept-Based Decision Trees from CNNs Conner Chyung Michael Tsang Yan Liu FAtt 41 8 0 11 Jun 2019
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding Haotian Ma Hao Zhang Fan Zhou Yinqing Zhang Quanshi Zhang FAtt 23 0 0 10 Jun 2019
Is Attention Interpretable? Sofia Serrano Noah A. Smith 112 687 0 09 Jun 2019
Proposed Guidelines for the Responsible Use of Explainable Machine Learning Patrick Hall Navdeep Gill N. Schmidt SILM XAI FaML 77 29 0 08 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 93 101 0 08 Jun 2019
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness Walt Woods Jack H Chen C. Teuscher AAML 66 46 0 07 Jun 2019
XRAI: Better Attributions Through Regions A. Kapishnikov Tolga Bolukbasi Fernanda Viégas Michael Terry FAtt XAI 74 213 0 06 Jun 2019
Survey on Publicly Available Sinhala Natural Language Processing Tools and Research Nisansa de Silva 224 45 0 05 Jun 2019
Evaluating Explanation Methods for Deep Learning in Security Alexander Warnecke Dan Arp Christian Wressnegger Konrad Rieck XAI AAML FAtt 71 94 0 05 Jun 2019
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation Minh Nhat Vu Truc D. T. Nguyen Nhathai Phan Ralucca Gera My T. Thai AAML FAtt 77 22 0 05 Jun 2019
Interpretable and Differentially Private Predictions Frederik Harder Matthias Bauer Mijung Park FAtt 71 53 0 05 Jun 2019
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse David Jurgens Eshwar Chandrasekharan Libby Hemphill 85 138 0 04 Jun 2019
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization Yichang Wang Rémi Emonet Elisa Fromont S. Malinowski Etienne Ménager Loic Mosser R. Tavenard AI4TS 43 12 0 03 Jun 2019
Model Agnostic Contrastive Explanations for Structured Data Amit Dhurandhar Tejaswini Pedapati Avinash Balakrishnan Pin-Yu Chen Karthikeyan Shanmugam Ruchi Puri FAtt 88 83 0 31 May 2019
Do Human Rationales Improve Machine Explanations? Julia Strout Ye Zhang Raymond J. Mooney 84 58 0 31 May 2019
Explainability Techniques for Graph Convolutional Networks Federico Baldassarre Hossein Azizpour GNN FAtt 178 272 0 31 May 2019
Leveraging Latent Features for Local Explanations Ronny Luss Pin-Yu Chen Amit Dhurandhar P. Sattigeri Yunfeng Zhang Karthikeyan Shanmugam Chun-Chen Tu FAtt 115 38 0 29 May 2019
Learning Representations by Humans, for Humans Sophie Hilgard Nir Rosenfeld M. Banaji Jack Cao David C. Parkes OCL HAI AI4CE 103 29 0 29 May 2019
Generation of Policy-Level Explanations for Reinforcement Learning Nicholay Topin Manuela Veloso 75 75 0 28 May 2019
Adversarial Robustness Guarantees for Classification with Gaussian Processes Arno Blaas A. Patané Luca Laurenti L. Cardelli Marta Z. Kwiatkowska Stephen J. Roberts GP AAML 89 21 0 28 May 2019
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction Diane Bouchacourt Ludovic Denoyer FAtt 74 21 0 28 May 2019
Analyzing the Interpretability Robustness of Self-Explaining Models Haizhong Zheng Earlence Fernandes A. Prakash AAML LRM 76 7 0 27 May 2019
Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction Sheikh Rabiul Islam W. Eberle Sid Bundy S. Ghafoor MLAU 69 23 0 27 May 2019
Interpretable Neural Predictions with Differentiable Binary Variables Jasmijn Bastings Wilker Aziz Ivan Titov 89 215 0 20 May 2019
The Twin-System Approach as One Generic Solution for XAI: An Overview of ANN-CBR Twins for Explaining Deep Learning Mark T. Keane Eoin M. Kenny 81 13 0 20 May 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 140 1,032 0 19 May 2019
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees Summer Devlin Chandan Singh W. James Murdoch Bin Yu FAtt 54 14 0 18 May 2019
How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins Mark T. Keane Eoin M. Kenny 125 81 0 17 May 2019
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices Jessica Morley Luciano Floridi Libby Kinsey Anat Elhalal 83 57 0 15 May 2019
Modelling urban networks using Variational Autoencoders Kira Kempinska R. Murcio GNN 42 39 0 14 May 2019
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use S. Tonekaboni Shalmali Joshi M. Mccradden Anna Goldenberg 97 400 0 13 May 2019
Explainable AI for Trees: From Local Explanations to Global Understanding Scott M. Lundberg G. Erion Hugh Chen A. DeGrave J. Prutkin B. Nair R. Katz J. Himmelfarb N. Bansal Su-In Lee FAtt 106 291 0 11 May 2019
Interpret Federated Learning with Shapley Values Guan Wang FAtt FedML 71 92 0 11 May 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 139 19 0 10 May 2019
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges Rob Ashmore R. Calinescu Colin Paterson AI4TS 73 119 0 10 May 2019
Embedding Human Knowledge into Deep Neural Network via Attention Map Masahiro Mitsuhara Hiroshi Fukui Yusuke Sakashita Takanori Ogata Tsubasa Hirakawa Takayoshi Yamashita H. Fujiyoshi 95 73 0 09 May 2019