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A Survey Of Methods For Explaining Black Box Models

6 February 2018

Papers citing "A Survey Of Methods For Explaining Black Box Models"

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Title
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms Giorgio Visani Enrico Bagli F. Chesani FAtt 78 60 0 10 Jun 2020
Principles to Practices for Responsible AI: Closing the Gap Daniel S. Schiff B. Rakova A. Ayesh Anat Fanti M. Lennon 89 89 0 08 Jun 2020
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach Christoph Molnar Gunnar Konig B. Bischl Giuseppe Casalicchio 83 84 0 08 Jun 2020
Evaluation of Similarity-based Explanations Kazuaki Hanawa Sho Yokoi Satoshi Hara Kentaro Inui XAI 34 2 0 08 Jun 2020
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI Antonio Bărbălău Adrian Cosma Radu Tudor Ionescu Marius Popescu 47 9 0 06 Jun 2020
SHADOWCAST: Controllable Graph Generation W. Tann E. Chang Bryan Hooi 74 2 0 06 Jun 2020
MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations Qing Yang Xia Zhu Jong-Kae Fwu Yun Ye Ganmei You Yuan Zhu AAML 51 20 0 04 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 110 271 0 29 May 2020
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers Kevin Fauvel Véronique Masson Elisa Fromont AI4TS 77 17 0 29 May 2020
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI) Mark T. Keane Barry Smyth CML 76 146 0 26 May 2020
Adversarial NLI for Factual Correctness in Text Summarisation Models Mario Barrantes Benedikt Herudek Richard Wang 52 17 0 24 May 2020
Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles Mário Popolin Neto F. Paulovich FAtt 81 89 0 08 May 2020
Contextualizing Hate Speech Classifiers with Post-hoc Explanation Brendan Kennedy Xisen Jin Aida Mostafazadeh Davani Morteza Dehghani Xiang Ren 135 142 0 05 May 2020
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition Mahsan Nourani Chiradeep Roy Tahrima Rahman Eric D. Ragan Nicholas Ruozzi Vibhav Gogate AAML 62 18 0 05 May 2020
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds M. Kovalev Lev V. Utkin AAML 81 32 0 05 May 2020
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models Lev V. Utkin M. Kovalev E. Kasimov 53 10 0 05 May 2020
Post-hoc explanation of black-box classifiers using confident itemsets M. Moradi Matthias Samwald 139 101 0 05 May 2020
Construction and Elicitation of a Black Box Model in the Game of Bridge V. Ventos Daniel A. Braun Colin Deheeger Jean Pierre Desmoulins Jean Baptiste Fantun Swann Legras Alexis Rimbaud C. Rouveirol H. Soldano Solène Thépaut 32 0 0 04 May 2020
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware? Marco Melis Michele Scalas Ambra Demontis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 54 28 0 04 May 2020
WT5?! Training Text-to-Text Models to Explain their Predictions Sharan Narang Colin Raffel Katherine Lee Adam Roberts Noah Fiedel Karishma Malkan 82 201 0 30 Apr 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 114 380 0 30 Apr 2020
Valid Explanations for Learning to Rank Models Jaspreet Singh Zhenye Wang Megha Khosla Avishek Anand LRM FAtt 43 8 0 29 Apr 2020
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models Jayaraman J. Thiagarajan P. Sattigeri Deepta Rajan Bindya Venkatesh MedIm 33 17 0 27 Apr 2020
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation Bozhi Wu Sen Chen Cuiyun Gao Lingling Fan Yang Liu W. Wen Michael R. Lyu 95 58 0 24 Apr 2020
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs Sungsoo Ray Hong Jessica Hullman E. Bertini HAI 92 197 0 23 Apr 2020
Learning a Formula of Interpretability to Learn Interpretable Formulas M. Virgolin A. D. Lorenzo Eric Medvet Francesca Randone 60 35 0 23 Apr 2020
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review F. Sado C. K. Loo W. S. Liew Matthias Kerzel S. Wermter 98 52 0 21 Apr 2020
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? Alon Jacovi Yoav Goldberg XAI 140 601 0 07 Apr 2020
A general framework for inference on algorithm-agnostic variable importance B. Williamson P. Gilbert N. Simon M. Carone FAtt CML 56 68 0 07 Apr 2020
A New Method to Compare the Interpretability of Rule-based Algorithms Vincent Margot G. Luta FAtt 20 17 0 03 Apr 2020
Born-Again Tree Ensembles Thibaut Vidal Toni Pacheco Maximilian Schiffer 140 54 0 24 Mar 2020
Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems Kengo Tajiri Yasuhiro Ikeda Yuusuke Nakano Keishiro Watanabe 18 1 0 24 Mar 2020
Interpretable machine learning models: a physics-based view Ion Matei Johan de Kleer C. Somarakis R. Rai John S. Baras PINN AI4CE 36 1 0 22 Mar 2020
Layerwise Knowledge Extraction from Deep Convolutional Networks S. Odense Artur Garcez FAtt 49 10 0 19 Mar 2020
SurvLIME: A method for explaining machine learning survival models M. Kovalev Lev V. Utkin E. Kasimov 292 91 0 18 Mar 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 141 83 0 17 Mar 2020
Harnessing Explanations to Bridge AI and Humans Vivian Lai Samuel Carton Chenhao Tan 54 5 0 16 Mar 2020
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning Wenjie Shi Gao Huang Shiji Song Zhuoyuan Wang Tingyu Lin Cheng Wu SSL 76 18 0 16 Mar 2020
LIMEADE: From AI Explanations to Advice Taking Benjamin Charles Germain Lee Doug Downey Kyle Lo Daniel S. Weld 167 6 0 09 Mar 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 112 96 0 05 Mar 2020
Robot Mindreading and the Problem of Trust Andrés Páez 25 0 0 02 Mar 2020
A general framework for scientifically inspired explanations in AI David Tuckey A. Russo Krysia Broda 33 0 0 02 Mar 2020
Testing Monotonicity of Machine Learning Models Arnab Sharma Heike Wehrheim 72 9 0 27 Feb 2020
Better Classifier Calibration for Small Data Sets Alasalmi Tuomo Jaakko Suutala Heli Koskimäki J. Röning 20 9 0 24 Feb 2020
Sampling for Deep Learning Model Diagnosis (Technical Report) Parmita Mehta S. Portillo Magdalena Balazinska Andrew J. Connolly LM&MA MLAU 26 2 0 22 Feb 2020
The Pragmatic Turn in Explainable Artificial Intelligence (XAI) Andrés Páez 68 197 0 22 Feb 2020
Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy M. Virgolin Z. Wang B. Balgobind I. V. Dijk J. Wiersma ... L. Zaletel C. Rasch A. Bel Peter A. N. Bosman Tanja Alderliesten 10 5 0 17 Feb 2020
AI safety: state of the field through quantitative lens Mislav Juric A. Sandic Mario Brčič 93 24 0 12 Feb 2020
Convex Density Constraints for Computing Plausible Counterfactual Explanations André Artelt Barbara Hammer 74 47 0 12 Feb 2020
Leveraging Rationales to Improve Human Task Performance Devleena Das Sonia Chernova 77 50 0 11 Feb 2020