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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
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
An Information Theoretic Interpretation to Deep Neural Networks Shao-Lun Huang Xiangxiang Xu Lizhong Zheng G. Wornell FAtt 90 44 0 16 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
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use S. Tonekaboni Shalmali Joshi M. Mccradden Anna Goldenberg 97 401 0 13 May 2019
Property Inference for Deep Neural Networks D. Gopinath Hayes Converse C. Păsăreanu Ankur Taly 59 8 0 29 Apr 2019
Explaining a prediction in some nonlinear models Cosimo Izzo FAtt 21 0 0 21 Apr 2019
"Why did you do that?": Explaining black box models with Inductive Synthesis Görkem Paçaci David Johnson S. McKeever A. Hamfelt 35 6 0 17 Apr 2019
Explainability in Human-Agent Systems A. Rosenfeld A. Richardson XAI 83 207 0 17 Apr 2019
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability Christoph Molnar Giuseppe Casalicchio B. Bischl FAtt 54 60 0 08 Apr 2019
Visualization of Convolutional Neural Networks for Monocular Depth Estimation Junjie Hu Yan Zhang Takayuki Okatani MDE 124 83 0 06 Apr 2019
An Attentive Survey of Attention Models S. Chaudhari Varun Mithal Gungor Polatkan R. Ramanath 192 666 0 05 Apr 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 163 1,336 0 10 Mar 2019
Challenges for an Ontology of Artificial Intelligence Scott H. Hawley 23 11 0 25 Feb 2019
Significance Tests for Neural Networks Enguerrand Horel K. Giesecke 57 56 0 16 Feb 2019
RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter Michele Mazza S. Cresci Marco Avvenuti Walter Quattrociocchi Maurizio Tesconi 64 197 0 12 Feb 2019
Assessing the Local Interpretability of Machine Learning Models Dylan Slack Sorelle A. Friedler C. Scheidegger Chitradeep Dutta Roy FAtt 60 71 0 09 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 126 205 0 06 Feb 2019
Attention in Natural Language Processing Andrea Galassi Marco Lippi Paolo Torroni GNN 73 481 0 04 Feb 2019
Interpreting Deep Neural Networks Through Variable Importance J. Ish-Horowicz Dana Udwin Seth Flaxman Sarah Filippi Lorin Crawford FAtt 52 14 0 28 Jan 2019
Fairwashing: the risk of rationalization Ulrich Aïvodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 70 148 0 28 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 211 1,457 0 14 Jan 2019
Personalized explanation in machine learning: A conceptualization J. Schneider J. Handali XAI FAtt 80 17 0 03 Jan 2019
LEAFAGE: Example-based and Feature importance-based Explanationsfor Black-box ML models Ajaya Adhikari David Tax R. Satta M. Faeth FAtt 111 11 0 21 Dec 2018
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning Mirko Polato F. Aiolli FAtt 19 8 0 19 Dec 2018
An Interpretable Model with Globally Consistent Explanations for Credit Risk Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang FAtt 87 94 0 30 Nov 2018
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems Sina Mohseni Niloofar Zarei Eric D. Ragan 122 102 0 28 Nov 2018
Detecting Token Systems on Ethereum Michael Fröwis A. Fuchs Rainer Böhme 131 50 0 28 Nov 2018
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems O. Lahav Nicholas Mastronarde M. Schaar 64 30 0 27 Nov 2018
Interpretable Credit Application Predictions With Counterfactual Explanations Rory Mc Grath Luca Costabello Chan Le Van Paul Sweeney F. Kamiab Zhao Shen Freddy Lecue FAtt 81 109 0 13 Nov 2018
YASENN: Explaining Neural Networks via Partitioning Activation Sequences Yaroslav Zharov Denis Korzhenkov J. Lyu Alexander Tuzhilin FAtt AAML 23 6 0 07 Nov 2018
Deep Weighted Averaging Classifiers Dallas Card Michael J.Q. Zhang Hao Tang 94 41 0 06 Nov 2018
Semantic bottleneck for computer vision tasks Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard 92 17 0 06 Nov 2018
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks Davide Maiorca Battista Biggio Giorgio Giacinto AAML 69 47 0 02 Nov 2018
On The Stability of Interpretable Models Riccardo Guidotti Salvatore Ruggieri FAtt 64 10 0 22 Oct 2018
Concise Explanations of Neural Networks using Adversarial Training P. Chalasani Jiefeng Chen Aravind Sadagopan S. Jha Xi Wu AAML FAtt 162 13 0 15 Oct 2018
Explaining Black Boxes on Sequential Data using Weighted Automata Stéphane Ayache Rémi Eyraud Noé Goudian 69 44 0 12 Oct 2018
On the Art and Science of Machine Learning Explanations Patrick Hall FAtt XAI 92 30 0 05 Oct 2018
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees Klaus Broelemann Gjergji Kasneci 84 20 0 25 Sep 2018
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts Samuel Carton Qiaozhu Mei Paul Resnick FAtt AAML 124 34 0 01 Sep 2018
Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262 Rick Salay Krzysztof Czarnecki 104 70 0 05 Aug 2018
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences J. V. D. Waa J. Diggelen K. Bosch Mark Antonius Neerincx OffRL 73 109 0 23 Jul 2018
Open the Black Box Data-Driven Explanation of Black Box Decision Systems D. Pedreschi F. Giannotti Riccardo Guidotti A. Monreale Luca Pappalardo Salvatore Ruggieri Franco Turini 114 38 0 26 Jun 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 143 166 0 20 Jun 2018
Defining Locality for Surrogates in Post-hoc Interpretablity Thibault Laugel X. Renard Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki FAtt 94 80 0 19 Jun 2018
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 79 82 0 19 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 124 1,869 0 31 May 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 144 440 0 28 May 2018
Faithfully Explaining Rankings in a News Recommender System Maartje ter Hoeve Anne Schuth Daan Odijk Maarten de Rijke OffRL 43 24 0 14 May 2018
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World Yoshihide Sawada DRL 68 10 0 19 Apr 2018
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models Tomáš Kliegr Š. Bahník Johannes Furnkranz 106 105 0 09 Apr 2018