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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
Identifying Spurious Correlations for Robust Text Classification Zhao Wang A. Culotta OOD 80 78 0 06 Oct 2020
A Survey on Explainability in Machine Reading Comprehension Mokanarangan Thayaparan Marco Valentino André Freitas FaML 108 49 0 01 Oct 2020
Explainable AI without Interpretable Model Kary Framling ELM 27 7 0 29 Sep 2020
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach Rémi Eyraud Stéphane Ayache 51 16 0 28 Sep 2020
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing Nijat Mehdiyev Peter Fettke 75 11 0 22 Sep 2020
Machine Guides, Human Supervises: Interactive Learning with Global Explanations Teodora Popordanoska Mohit Kumar Stefano Teso 118 21 0 21 Sep 2020
Humans learn too: Better Human-AI Interaction using Optimized Human Inputs Johannes Schneider 35 4 0 19 Sep 2020
Explainable boosted linear regression for time series forecasting Igor Ilic Berk Görgülü Mucahit Cevik M. Baydogan AI4TS 56 63 0 18 Sep 2020
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting Ozan Ozyegen Igor Ilic Mucahit Cevik FAtt AI4TS 41 2 0 18 Sep 2020
Better Model Selection with a new Definition of Feature Importance Fan Fang Carmine Ventre Lingbo Li Leslie Kanthan Fan Wu Michail Basios FAtt 42 6 0 16 Sep 2020
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations F. Sperrle Mennatallah El-Assady G. Guo Duen Horng Chau Alex Endert Daniel A. Keim 69 19 0 14 Sep 2020
Genetic Programming is Naturally Suited to Evolve Bagging Ensembles M. Virgolin 38 0 0 13 Sep 2020
Towards Interpretable Multi-Task Learning Using Bilevel Programming Francesco Alesiani Shujian Yu Ammar Shaker Wenzhe Yin 23 4 0 11 Sep 2020
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning Eoin M. Kenny Mark T. Keane 70 102 0 10 Sep 2020
Region Comparison Network for Interpretable Few-shot Image Classification Z. Xue Lixin Duan Wen Li Lin Chen Jiebo Luo 61 16 0 08 Sep 2020
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring Nijat Mehdiyev Peter Fettke AI4TS 67 55 0 04 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 87 52 0 03 Sep 2020
Machine Reasoning Explainability K. Čyras R. Badrinath S. Mohalik A. Mujumdar Alexandros Nikou Alessandro Previti Vaishnavi Sundararajan Aneta Vulgarakis Feljan LRM 72 13 0 01 Sep 2020
SOAR: Simultaneous Or of And Rules for Classification of Positive & Negative Classes Elena Khusainova Emily Dodwell Ritwik Mitra 29 2 0 25 Aug 2020
Looking Deeper into Tabular LIME Damien Garreau U. V. Luxburg FAtt LMTD 173 30 0 25 Aug 2020
DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data Zhaocheng Liu Qiang Liu Haoli Zhang Yuntian Chen 84 13 0 22 Aug 2020
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis Ming Fan Wenying Wei Xiaofei Xie Yang Liu X. Guan Ting Liu FAtt AAML 102 38 0 13 Aug 2020
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Sasha Rubin Thomas Gerspacher Martin C. Cooper Alexey Ignatiev Nina Narodytska FAtt 72 63 0 13 Aug 2020
Bias and Discrimination in AI: a cross-disciplinary perspective Xavier Ferrer Tom van Nuenen Jose Such Mark Coté Natalia Criado FaML 48 148 0 11 Aug 2020
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs Ruigang Fu Qingyong Hu Xiaohu Dong Yulan Guo Yinghui Gao Biao Li FAtt 93 272 0 05 Aug 2020
Explainable Predictive Process Monitoring Musabir Musabayli F. Maggi Williams Rizzi Josep Carmona Chiara Di Francescomarino 68 61 0 04 Aug 2020
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution A. Khademi Vasant Honavar CML 39 9 0 01 Aug 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 89 471 0 31 Jul 2020
Computing Optimal Decision Sets with SAT Jinqiang Yu Alexey Ignatiev Peter Stuckey P. L. Bodic FAtt 114 26 0 29 Jul 2020
Memory networks for consumer protection:unfairness exposed Federico Ruggeri F. Lagioia Marco Lippi Paolo Torroni 26 0 0 24 Jul 2020
Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset M. Ahsan Kishor Datta Gupta Mohammad Maminur Islam Sajib Sen Md Lutfar Rahman Mohammad Shakhawat Hossain 52 35 0 24 Jul 2020
Deep Active Learning by Model Interpretability Qiang Liu Zhaocheng Liu Xiaofang Zhu Yeliang Xiu 49 4 0 23 Jul 2020
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance Mattia Carletti M. Terzi Gian Antonio Susto 50 42 0 21 Jul 2020
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models Catarina Moreira Yu-Liang Chou M. Velmurugan Chun Ouyang Renuka Sindhgatta P. Bruza 114 58 0 21 Jul 2020
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together G. Chan E. Bertini L. G. Nonato Brian Barr Claudio T. Silva 40 17 0 21 Jul 2020
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning Teodora Popordanoska Mohit Kumar Stefano Teso 46 1 0 20 Jul 2020
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Vladimiro González-Zelaya P. Missier Magdalene Ng Aad van Moorsel 72 18 0 17 Jul 2020
On quantitative aspects of model interpretability An-phi Nguyen María Rodríguez Martínez 67 115 0 15 Jul 2020
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates Roel Henckaerts Katrien Antonio Marie-Pier Côté 25 3 0 14 Jul 2020
Editable AI: Mixed Human-AI Authoring of Code Patterns Kartik Chugh Andrea Y. Solis Thomas D. Latoza 16 3 0 12 Jul 2020
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets Junsuk Choe Seong Joon Oh Sanghyuk Chun Seungho Lee Zeynep Akata Hyunjung Shim WSOL 460 25 0 08 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 195 645 0 01 Jul 2020
Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition Models Sahar Siddiqui E. Sizikova Gemma Roig N. Majaj D. Pelli 16 1 0 30 Jun 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 117 19 0 26 Jun 2020
DOME: Recommendations for supervised machine learning validation in biology Ian Walsh D. Fishman Dario Garcia-Gasulla T. Titma Gianluca Pollastri The ELIXIR Machine Learning focus group J. Harrow Fotis Psomopoulos Silvio C.E. Tosatto AI4CE 8 3 0 25 Jun 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 96 73 0 24 Jun 2020
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction Esther Puyol-Antón Chong Chen J. Clough B. Ruijsink B. Sidhu ... M. Elliott Vishal S. Mehta Daniel Rueckert C. Rinaldi A. King 58 33 0 24 Jun 2020
Quality Management of Machine Learning Systems P. Santhanam 39 18 0 16 Jun 2020
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 143 32 0 16 Jun 2020
A framework for step-wise explaining how to solve constraint satisfaction problems B. Bogaerts Emilio Gamba Tias Guns LRM 24 17 0 11 Jun 2020