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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
Reducing Unintended Bias of ML Models on Tabular and Textual Data Guilherme Alves M. Amblard Fabien Bernier Miguel Couceiro A. Napoli FaML 39 17 0 05 Aug 2021
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms Martin Pawelczyk Sascha Bielawski J. V. D. Heuvel Tobias Richter Gjergji Kasneci CML 96 105 0 02 Aug 2021
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations Upol Ehsan Samir Passi Q. V. Liao Larry Chan I-Hsiang Lee Michael J. Muller Mark O. Riedl 97 96 0 28 Jul 2021
A Reflection on Learning from Data: Epistemology Issues and Limitations Ahmad Hammoudeh Sara Tedmori Nadim Obeid 45 4 0 28 Jul 2021
Resisting Out-of-Distribution Data Problem in Perturbation of XAI Luyu Qiu Yi Yang Caleb Chen Cao Jing Liu Yueyuan Zheng H. Ngai J. H. Hsiao Lei Chen 73 18 0 27 Jul 2021
Desiderata for Explainable AI in statistical production systems of the European Central Bank Carlos Navarro Georgios Kanellos Thomas Gottron 47 10 0 18 Jul 2021
A Survey on Bias in Visual Datasets Simone Fabbrizzi Symeon Papadopoulos Eirini Ntoutsi Y. Kompatsiaris 203 129 0 16 Jul 2021
Explainable AI Enabled Inspection of Business Process Prediction Models Chun Ouyang Renuka Sindhgatta Catarina Moreira XAI 42 4 0 16 Jul 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 192 212 0 12 Jul 2021
From Common Sense Reasoning to Neural Network Models through Multiple Preferences: an overview Laura Giordano Valentina Gliozzi Daniele Theseider Dupré SSeg AI4CE 57 1 0 10 Jul 2021
Systematic human learning and generalization from a brief tutorial with explanatory feedback A. Nam James L. McClelland 38 1 0 10 Jul 2021
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice T. Vermeire Thibault Laugel X. Renard David Martens Marcin Detyniecki 37 16 0 09 Jul 2021
Levels of explainable artificial intelligence for human-aligned conversational explanations Richard Dazeley Peter Vamplew Cameron Foale Charlotte Young Sunil Aryal F. Cruz 65 93 0 07 Jul 2021
Does Dataset Complexity Matters for Model Explainers? J. Ribeiro R. Silva Lucas F. F. Cardoso Ronnie Cley de Oliveira Alves XAI ELM 28 14 0 06 Jul 2021
Understanding Consumer Preferences for Explanations Generated by XAI Algorithms Yanou Ramon T. Vermeire Olivier Toubia David Martens Theodoros Evgeniou 74 10 0 06 Jul 2021
ARM-Net: Adaptive Relation Modeling Network for Structured Data Shaofeng Cai Kaiping Zheng Gang Chen H. V. Jagadish Beng Chin Ooi Meihui Zhang 104 51 0 05 Jul 2021
Efficient Explanations for Knowledge Compilation Languages Xuanxiang Huang Yacine Izza Alexey Ignatiev Martin C. Cooper Nicholas M. Asher Sasha Rubin 61 15 0 04 Jul 2021
Productivity, Portability, Performance: Data-Centric Python Yiheng Wang Yao Zhang Yanzhang Wang Yan Wan Jiao Wang Zhongyuan Wu Yuhao Yang Bowen She 167 101 0 01 Jul 2021
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction Jiahua Rao Shuangjia Zheng Yuedong Yang 104 48 0 01 Jul 2021
Towards Model-informed Precision Dosing with Expert-in-the-loop Machine Learning Yihuang Kang Y. Chiu Ming-Yen Lin F. Su Sheng-Tai Huang 47 2 0 28 Jun 2021
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy Christopher J. Anders David Neumann Wojciech Samek K. Müller Sebastian Lapuschkin 107 66 0 24 Jun 2021
Rational Shapley Values David S. Watson 77 21 0 18 Jun 2021
NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights Kirill Bykov Anna Hedström Shinichi Nakajima Marina M.-C. Höhne FAtt 95 34 0 18 Jun 2021
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking Hang Jiang Sairam Gurajada Qiuhao Lu S. Neelam Lucian Popa Prithviraj Sen Yunyao Li Alexander G. Gray 53 25 0 17 Jun 2021
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data Lev V. Utkin A. Konstantinov Kirill Vishniakov FAtt 113 6 0 16 Jun 2021
Counterfactual Graphs for Explainable Classification of Brain Networks Carlo Abrate Francesco Bonchi CML 84 57 0 16 Jun 2021
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning M. Velmurugan Chun Ouyang Catarina Moreira Renuka Sindhgatta XAI 49 16 0 16 Jun 2021
A Framework for Evaluating Post Hoc Feature-Additive Explainers Zachariah Carmichael Walter J. Scheirer FAtt 75 4 0 15 Jun 2021
Keep CALM and Improve Visual Feature Attribution Jae Myung Kim Junsuk Choe Zeynep Akata Seong Joon Oh FAtt 480 20 0 15 Jun 2021
Counterfactual Explanations as Interventions in Latent Space Riccardo Crupi Alessandro Castelnovo D. Regoli Beatriz San Miguel González CML 44 24 0 14 Jun 2021
Characterizing the risk of fairwashing Ulrich Aïvodji Hiromi Arai Sébastien Gambs Satoshi Hara 92 28 0 14 Jun 2021
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features F. Ventura Salvatore Greco D. Apiletti Tania Cerquitelli 37 1 0 12 Jun 2021
Optimal Counterfactual Explanations in Tree Ensembles Axel Parmentier Thibaut Vidal 59 55 0 11 Jun 2021
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling M. T. Fischer Daniel Seebacher Rita Sevastjanova Daniel A. Keim Mennatallah El-Assady 49 10 0 11 Jun 2021
Explainable AI, but explainable to whom? Julie Gerlings Millie Søndergaard Jensen Arisa Shollo 80 43 0 10 Jun 2021
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching Tomas Folke Scott Cheng-Hsin Yang S. Anderson Patrick Shafto 51 19 0 08 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das K. Kenthapadi AAML 68 21 0 08 Jun 2021
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models Sahil Verma Keegan E. Hines John P. Dickerson 94 24 0 07 Jun 2021
Can a single neuron learn predictive uncertainty? Edgardo Solano-Carrillo UQCV 74 1 0 07 Jun 2021
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang 76 41 0 04 Jun 2021
On Efficiently Explaining Graph-Based Classifiers Xuanxiang Huang Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 118 39 0 02 Jun 2021
Is Sparse Attention more Interpretable? Clara Meister Stefan Lazov Isabelle Augenstein Ryan Cotterell MILM 64 45 0 02 Jun 2021
HisVA: A Visual Analytics System for Studying History Dongyun Han Gorakh Parsad Hwiyeon Kim Jaekyom Shim Oh-Sang Kwon Kyung A Son Jooyoung Lee Isaac Cho Sungahn Ko 46 9 0 01 Jun 2021
Efficient Explanations With Relevant Sets Yacine Izza Alexey Ignatiev Nina Narodytska Martin C. Cooper Sasha Rubin FAtt 94 16 0 01 Jun 2021
The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning K. Morik Helena Kotthaus Lukas Heppe Danny Heinrich Raphael Fischer Andrea Pauly Nico Piatkowski 104 4 0 01 Jun 2021
A Clarification of the Nuances in the Fairness Metrics Landscape Alessandro Castelnovo Riccardo Crupi Greta Greco D. Regoli Ilaria Giuseppina Penco A. Cosentini FaML 59 192 0 01 Jun 2021
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 81 68 0 01 Jun 2021
Understanding peacefulness through the world news Vasiliki Voukelatou Ioanna Miliou F. Giannotti Luca Pappalardo 34 0 0 01 Jun 2021
Explainability via Interactivity? Supporting Nonexperts' Sensemaking of Pretrained CNN by Interacting with Their Daily Surroundings Chao Wang Pengcheng An HAI 60 7 0 31 May 2021
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness Ruiwen Li Zhibo Zhang Jiani Li C. Trabelsi Scott Sanner Jongseong Jang Yeonjeong Jeong Dongsub Shim AAML 40 1 0 29 May 2021