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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
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects Samir Passi S. Jackson 233 115 0 09 Feb 2020
Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support Christian Meske Enrico Bunde 50 7 0 04 Feb 2020
Four Principles of Explainable AI as Applied to Biometrics and Facial Forensic Algorithms P. Phillips Mark A. Przybocki CVBM 38 14 0 03 Feb 2020
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study Ahmed Alqaraawi M. Schuessler Philipp Weiß Enrico Costanza N. Bianchi-Berthouze AAML FAtt XAI 72 200 0 03 Feb 2020
Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models Giorgio Visani Enrico Bagli F. Chesani A. Poluzzi D. Capuzzo FAtt 66 170 0 31 Jan 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space Riccardo Guidotti A. Monreale Stan Matwin D. Pedreschi FAtt 112 67 0 27 Jan 2020
Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective F. Emmert-Streib O. Yli-Harja M. Dehmer 37 85 0 26 Jan 2020
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience Bhavya Ghai Q. V. Liao Yunfeng Zhang Rachel K. E. Bellamy Klaus Mueller 94 29 0 24 Jan 2020
Evaluating Weakly Supervised Object Localization Methods Right Junsuk Choe Seong Joon Oh Seungho Lee Sanghyuk Chun Zeynep Akata Hyunjung Shim WSOL 395 189 0 21 Jan 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 147 213 0 15 Jan 2020
A Formal Approach to Explainability Lior Wolf Tomer Galanti Tamir Hazan FAtt GAN 69 22 0 15 Jan 2020
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans Vivian Lai Han Liu Chenhao Tan 90 143 0 14 Jan 2020
Explaining the Explainer: A First Theoretical Analysis of LIME Damien Garreau U. V. Luxburg FAtt 61 181 0 10 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 94 317 0 08 Jan 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 140 726 0 08 Jan 2020
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making Yunfeng Zhang Q. V. Liao Rachel K. E. Bellamy 117 687 0 07 Jan 2020
Teaching Responsible Data Science: Charting New Pedagogical Territory Julia Stoyanovich Armanda Lewis 49 39 0 23 Dec 2019
Exploring Interpretability for Predictive Process Analytics Renuka Sindhgatta Chun Ouyang Catarina Moreira 30 2 0 22 Dec 2019
Learning Deep Attribution Priors Based On Prior Knowledge Ethan Weinberger Joseph D. Janizek Su-In Lee FAtt 42 1 0 20 Dec 2019
Meta Decision Trees for Explainable Recommendation Systems Eyal Shulman Lior Wolf 27 18 0 19 Dec 2019
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation Tianhong Dai Kai Arulkumaran Tamara Gerbert Samyakh Tukra Feryal M. P. Behbahani Anil Anthony Bharath 84 28 0 18 Dec 2019
Differentiable Reasoning on Large Knowledge Bases and Natural Language Pasquale Minervini Matko Bovsnjak Tim Rocktaschel Sebastian Riedel Edward Grefenstette LRM 116 91 0 17 Dec 2019
Balancing the Tradeoff Between Clustering Value and Interpretability Sandhya Saisubramanian Sainyam Galhotra S. Zilberstein 72 41 0 17 Dec 2019
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group) Zied Bouraoui Antoine Cornuéjols Thierry Denoeux Sebastien Destercke Didier Dubois ... Jérôme Mengin H. Prade Steven Schockaert M. Serrurier Christel Vrain 128 14 0 13 Dec 2019
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis Francesco Concas Julien Mineraud Eemil Lagerspetz Samu Varjonen Xiaoli Liu Kai Puolamäki Petteri Nurmi Sasu Tarkoma 16 0 0 13 Dec 2019
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information Lukas Pfannschmidt Jonathan Jakob Fabian Hinder Michael Biehl Peter Tiño Barbara Hammer 13 2 0 10 Dec 2019
Knowledge extraction from the learning of sequences in a long short term memory (LSTM) architecture Ikram Chraibi Kaadoud N. Rougier F. Alexandre 16 22 0 06 Dec 2019
Automated Dependence Plots David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar 64 1 0 02 Dec 2019
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications Xiao Zhang Manish Marwah I-Ta Lee M. Arlitt Dan Goldwasser 42 14 0 01 Dec 2019
The relationship between trust in AI and trustworthy machine learning technologies Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Carlos Vladimiro Gonzalez Zelaya Aad van Moorsel FaML 85 262 0 27 Nov 2019
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey Vanessa Buhrmester David Münch Michael Arens MLAU FaML XAI AAML 114 367 0 27 Nov 2019
A psychophysics approach for quantitative comparison of interpretable computer vision models F. Biessmann D. Refiano 60 5 0 24 Nov 2019
LionForests: Local Interpretation of Random Forests Ioannis Mollas Nick Bassiliades I. Vlahavas Grigorios Tsoumakas 87 12 0 20 Nov 2019
An explanation method for Siamese neural networks Lev V. Utkin M. Kovalev E. Kasimov 59 15 0 18 Nov 2019
Information Bottleneck Theory on Convolutional Neural Networks Jianing Li Ding Liu FAtt 52 3 0 09 Nov 2019
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction Thai V. Le Suhang Wang Dongwon Lee 45 1 0 05 Nov 2019
What Gets Echoed? Understanding the "Pointers" in Explanations of Persuasive Arguments D. Atkinson K. Srinivasan Chenhao Tan 74 16 0 01 Nov 2019
Explanation by Progressive Exaggeration Sumedha Singla Brian Pollack Junxiang Chen Kayhan Batmanghelich FAtt MedIm 130 103 0 01 Nov 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 176 6,366 0 22 Oct 2019
Contextual Prediction Difference Analysis for Explaining Individual Image Classifications Jindong Gu Volker Tresp FAtt 49 8 0 21 Oct 2019
Identifying the Most Explainable Classifier Brett Mullins FAtt 59 1 0 18 Oct 2019
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification Vivian Lai Zheng Jon Cai Chenhao Tan FAtt 57 19 0 18 Oct 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan O. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 316 307 0 17 Oct 2019
Uncertainty-aware Sensitivity Analysis Using Rényi Divergences Topi Paananen Michael Riis Andersen Aki Vehtari 37 3 0 17 Oct 2019
Extracting Incentives from Black-Box Decisions Yonadav Shavit William S. Moses 39 9 0 13 Oct 2019
NLS: an accurate and yet easy-to-interpret regression method Victor Coscrato M. Inácio T. Botari Rafael Izbicki FAtt 46 4 0 11 Oct 2019
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases Maximilian Idahl Megha Khosla Avishek Anand 33 10 0 11 Oct 2019
Interpreting Deep Learning-Based Networking Systems Zili Meng Minhu Wang Jia-Ju Bai Mingwei Xu Hongzi Mao Hongxin Hu AI4CE 38 3 0 09 Oct 2019
Learn to Explain Efficiently via Neural Logic Inductive Learning Yu’an Yang Le Song NAI 97 77 0 06 Oct 2019
REDS: Rule Extraction for Discovering Scenarios Vadim Arzamasov Klemens Böhm 23 7 0 03 Oct 2019