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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
The Bouncer Problem: Challenges to Remote Explainability Erwan Le Merrer Gilles Tredan 63 8 0 03 Oct 2019
Leveraging Model Interpretability and Stability to increase Model Robustness Leilei Gan T. Michel Alexandre Briot AAML FAtt 27 1 0 01 Oct 2019
Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets Suryabhan Singh Hada M. A. Carreira-Perpiñán BDL 23 8 0 27 Sep 2019
Adversarial ML Attack on Self Organizing Cellular Networks Salah-ud-din Farooq Muhammad Usama Junaid Qadir M. Imran AAML 18 3 0 26 Sep 2019
Towards Explainability for a Civilian UAV Fleet Management using an Agent-based Approach Yazan Mualla A. Najjar T. Kampik I. Tchappi Stéphane Galland Christophe Nicolle 19 4 0 22 Sep 2019
Empirical Analysis of Multi-Task Learning for Reducing Model Bias in Toxic Comment Detection Ameya Vaidya Feng Mai Yue Ning 160 21 0 21 Sep 2019
Towards Explainable Neural-Symbolic Visual Reasoning Adrien Bennetot J. Laurent Raja Chatila Natalia Díaz Rodríguez XAI 32 1 0 19 Sep 2019
Class Feature Pyramids for Video Explanation Alexandros Stergiou G. Kapidis Grigorios Kalliatakis C. Chrysoulas R. Poppe R. Veltkamp FAtt 54 19 0 18 Sep 2019
Towards a Rigorous Evaluation of XAI Methods on Time Series U. Schlegel Hiba Arnout Mennatallah El-Assady Daniela Oelke Daniel A. Keim XAI AI4TS 114 174 0 16 Sep 2019
Towards A Robot Explanation System: A Survey and Our Approach to State Summarization, Storage and Querying, and Human Interface Zhao Han Jordan Allspaw Adam Norton Holly Yanco 28 5 0 13 Sep 2019
How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations Betty van Aken B. Winter Alexander Loser Felix Alexander Gers 78 154 0 11 Sep 2019
Pluggable Social Artificial Intelligence for Enabling Human-Agent Teaming J. Diggelen Jonathan Barnhoorn Marieke M. M. Peeters Wessel van Staal M. L. Stolk B. Vecht J. V. D. Waa J. Schraagen LLMAG 17 15 0 10 Sep 2019
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation Hanjie Chen Yangfeng Ji 24 10 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
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms M. Virgolin Ziyuan Wang Tanja Alderliesten Peter A. N. Bosman 24 2 0 09 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
Personalization of Deep Learning Johannes Schneider M. Vlachos 103 37 0 06 Sep 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
Computing Linear Restrictions of Neural Networks Matthew Sotoudeh Aditya V. Thakur 41 24 0 17 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
Measurable Counterfactual Local Explanations for Any Classifier Adam White Artur Garcez FAtt 73 98 0 08 Aug 2019
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning M. Alzantot Amy Widdicombe S. Julier Mani B. Srivastava AAML FAtt 49 3 0 05 Aug 2019
Efficient computation of counterfactual explanations of LVQ models André Artelt Barbara Hammer 54 16 0 02 Aug 2019
A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room Zhicheng Cui Bradley A. Fritz C. King M. Avidan Yixin Chen 50 14 0 29 Jul 2019
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning Thilo Spinner U. Schlegel H. Schäfer Mennatallah El-Assady HAI 76 239 0 29 Jul 2019
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks Jingyuan Wang Yang Zhang Ke Tang Junjie Wu Zhang Xiong AIFin 55 121 0 24 Jul 2019
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 102 197 0 22 Jul 2019
Minimizing the expected value of the asymmetric loss and an inequality of the variance of the loss Naoya Yamaguchi Yuka Yamaguchi R. Nishii 10 2 0 18 Jul 2019
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting Ana Lucic H. Haned Maarten de Rijke 68 64 0 17 Jul 2019
Global Aggregations of Local Explanations for Black Box models I. V. D. Linden H. Haned Evangelos Kanoulas FAtt 71 66 0 05 Jul 2019
Explaining Predictions from Tree-based Boosting Ensembles Ana Lucic H. Haned Maarten de Rijke FAtt 46 5 0 04 Jul 2019
Consistent Regression using Data-Dependent Coverings Vincent Margot Jean-Patrick Baudry Frédéric Guilloux Olivier Wintenberger 52 5 0 04 Jul 2019
On Explaining Machine Learning Models by Evolving Crucial and Compact Features M. Virgolin Tanja Alderliesten Peter A. N. Bosman 70 28 0 04 Jul 2019
Model Bridging: Connection between Simulation Model and Neural Network Keiichi Kisamori Keisuke Yamazaki Yuto Komori Hiroshi Tokieda 41 1 0 22 Jun 2019
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions Charles Marx R. L. Phillips Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian TDI CML MLAU 67 27 0 20 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
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
LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding Ioannis Mollas Nikolaos Bassiliades Grigorios Tsoumakas 49 12 0 15 Jun 2019
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language Leon Weber Pasquale Minervini Jannes Munchmeyer Ulf Leser Tim Rocktaschel NAI LRM 59 96 0 14 Jun 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 74 363 0 11 Jun 2019
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 139 45 0 11 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
Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees X. Renard Nicolas Woloszko Jonathan Aigrain Marcin Detyniecki 45 11 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
Interpreting a Recurrent Neural Network's Predictions of ICU Mortality Risk L. Ho M. Aczon D. Ledbetter R. Wetzel 35 3 0 23 May 2019
Computationally Efficient Feature Significance and Importance for Machine Learning Models Enguerrand Horel K. Giesecke FAtt 55 9 0 23 May 2019
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning Andrea Galassi Kristian Kersting Marco Lippi Xiaoting Shao Paolo Torroni NAI 101 15 0 22 May 2019
Explainable Machine Learning for Scientific Insights and Discoveries R. Roscher B. Bohn Marco F. Duarte Jochen Garcke XAI 120 677 0 21 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
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models Shubham Sharma Jette Henderson Joydeep Ghosh 85 87 0 20 May 2019