A Survey Of Methods For Explaining Black Box Models

6 February 2018

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

50 / 419 papers shown

Title
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 28 32 0 16 Jun 2020
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms Giorgio Visani Enrico Bagli F. Chesani FAtt 27 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 19 87 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 31 77 0 08 Jun 2020
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers Kevin Fauvel Véronique Masson Elisa Fromont AI4TS 44 17 0 29 May 2020
Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles Mário Popolin Neto F. Paulovich FAtt 33 88 0 08 May 2020
Contextualizing Hate Speech Classifiers with Post-hoc Explanation Brendan Kennedy Xisen Jin Aida Mostafazadeh Davani Morteza Dehghani Xiang Ren 13 137 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 15 17 0 05 May 2020
Post-hoc explanation of black-box classifiers using confident itemsets M. Moradi Matthias Samwald 57 98 0 05 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 41 371 0 30 Apr 2020
Learning a Formula of Interpretability to Learn Interpretable Formulas M. Virgolin A. D. Lorenzo Eric Medvet Francesca Randone 16 33 0 23 Apr 2020
Born-Again Tree Ensembles Thibaut Vidal Toni Pacheco Maximilian Schiffer 62 53 0 24 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 44 82 0 17 Mar 2020
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning Wenjie Shi Gao Huang Shiji Song Zhuoyuan Wang Tingyu Lin Cheng Wu SSL 28 18 0 16 Mar 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 57 93 0 05 Mar 2020
Testing Monotonicity of Machine Learning Models Arnab Sharma Heike Wehrheim 6 8 0 27 Feb 2020
Better Classifier Calibration for Small Data Sets Alasalmi Tuomo Jaakko Suutala Heli Koskimäki J. Röning 13 9 0 24 Feb 2020
The Pragmatic Turn in Explainable Artificial Intelligence (XAI) Andrés Páez 13 190 0 22 Feb 2020
AI safety: state of the field through quantitative lens Mislav Juric A. Sandic Mario Brčič 25 24 0 12 Feb 2020
Convex Density Constraints for Computing Plausible Counterfactual Explanations André Artelt Barbara Hammer 19 47 0 12 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 19 7 0 04 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 27 197 0 03 Feb 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space Riccardo Guidotti A. Monreale Stan Matwin D. Pedreschi FAtt 14 67 0 27 Jan 2020
Evaluating Weakly Supervised Object Localization Methods Right Junsuk Choe Seong Joon Oh Seungho Lee Sanghyuk Chun Zeynep Akata Hyunjung Shim WSOL 300 186 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 37 207 0 15 Jan 2020
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans Vivian Lai Han Liu Chenhao Tan 35 138 0 14 Jan 2020
Explaining the Explainer: A First Theoretical Analysis of LIME Damien Garreau U. V. Luxburg FAtt 9 172 0 10 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 52 702 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 28 661 0 07 Jan 2020
Exploring Interpretability for Predictive Process Analytics Renuka Sindhgatta Chun Ouyang Catarina Moreira 8 2 0 22 Dec 2019
Differentiable Reasoning on Large Knowledge Bases and Natural Language Pasquale Minervini Matko Bovsnjak Tim Rocktaschel Sebastian Riedel Edward Grefenstette LRM 18 88 0 17 Dec 2019
Balancing the Tradeoff Between Clustering Value and Interpretability Sandhya Saisubramanian Sainyam Galhotra S. Zilberstein 24 40 0 17 Dec 2019
Automated Dependence Plots David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar 12 1 0 02 Dec 2019
LionForests: Local Interpretation of Random Forests Ioannis Mollas Nick Bassiliades I. Vlahavas Grigorios Tsoumakas 19 12 0 20 Nov 2019
An explanation method for Siamese neural networks Lev V. Utkin M. Kovalev E. Kasimov 19 14 0 18 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 37 6,111 0 22 Oct 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
Uncertainty-aware Sensitivity Analysis Using Rényi Divergences Topi Paananen Michael Riis Andersen Aki Vehtari 19 3 0 17 Oct 2019
Empirical Analysis of Multi-Task Learning for Reducing Model Bias in Toxic Comment Detection Ameya Vaidya Feng Mai Yue Ning 115 21 0 21 Sep 2019
LoRMIkA: Local rule-based model interpretability with k-optimal associations Dilini Sewwandi Rajapaksha Christoph Bergmeir Wray L. Buntine 35 31 0 11 Aug 2019
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning M. Alzantot Amy Widdicombe S. Julier Mani B. Srivastava AAML FAtt 15 3 0 05 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 22 13 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 16 119 0 24 Jul 2019
Global Aggregations of Local Explanations for Black Box models I. V. D. Linden H. Haned Evangelos Kanoulas FAtt 21 63 0 05 Jul 2019
On Explaining Machine Learning Models by Evolving Crucial and Compact Features M. Virgolin Tanja Alderliesten Peter A. N. Bosman 11 28 0 04 Jul 2019
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 107 44 0 11 Jun 2019
An Information Theoretic Interpretation to Deep Neural Networks Shao-Lun Huang Xiangxiang Xu Lizhong Zheng G. Wornell FAtt 22 41 0 16 May 2019
"Why did you do that?": Explaining black box models with Inductive Synthesis Görkem Paçaci David Johnson S. McKeever A. Hamfelt 15 6 0 17 Apr 2019
Explainability in Human-Agent Systems A. Rosenfeld A. Richardson XAI 27 203 0 17 Apr 2019