v1v2v3 (latest)

A Survey Of Methods For Explaining Black Box Models

6 February 2018

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

50 / 1,104 papers shown

Title
Improving healthcare access management by predicting patient no-show behaviour David Barrera Ferro S. Brailsford Cristián Bravo Honora K. Smith 38 35 0 10 Dec 2020
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 70 18 0 10 Dec 2020
On Shapley Credit Allocation for Interpretability Debraj Basu FAtt 26 2 0 10 Dec 2020
Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey Xiaoqin Zhang Yan Hu Zunjie Xiao Jiansheng Fang Risa Higashita Jiang-Dong Liu 141 41 0 09 Dec 2020
Methodology for Mining, Discovering and Analyzing Semantic Human Mobility Behaviors Clément Moreau T. Devogele Laurent Étienne Verónika Peralta Cyril de Runz 21 1 0 08 Dec 2020
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks U. Schlegel Daniela Oelke Daniel A. Keim Mennatallah El-Assady AI4TS 21 14 0 08 Dec 2020
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach M. Velmurugan Chun Ouyang Catarina Moreira Renuka Sindhgatta XAI 90 5 0 08 Dec 2020
Understanding Interpretability by generalized distillation in Supervised Classification Adit Agarwal Dr. K.K. Shukla Arjan Kuijper Anirban Mukhopadhyay FaML FAtt 36 0 0 05 Dec 2020
Challenging common interpretability assumptions in feature attribution explanations Jonathan Dinu Jeffrey P. Bigham J. Z. K. Unaffiliated 78 14 0 04 Dec 2020
Neural Prototype Trees for Interpretable Fine-grained Image Recognition Meike Nauta Ron van Bree C. Seifert 196 270 0 03 Dec 2020
Reviewing the Need for Explainable Artificial Intelligence (xAI) Julie Gerlings Arisa Shollo Ioanna D. Constantiou 61 73 0 02 Dec 2020
Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information F. Simini Gianni Barlacchi Massimilano Luca Luca Pappalardo HAI 98 194 0 01 Dec 2020
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations João Bento Pedro Saleiro André F. Cruz Mário A. T. Figueiredo P. Bizarro FAtt AI4TS 85 97 0 30 Nov 2020
Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation hsan Ullah André Ríos Vaibhav Gala Susan Mckeever FAtt 73 10 0 26 Nov 2020
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals Tsung-Yu Hsieh Suhang Wang Yiwei Sun Vasant Honavar BDL AI4TS FAtt 29 9 0 23 Nov 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 144 252 0 21 Nov 2020
PSD2 Explainable AI Model for Credit Scoring N. Torrent Giorgio Visani Engineering 22 1 0 20 Nov 2020
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 77 781 0 16 Nov 2020
Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science Adam J. Johs Denise E. Agosto Rosina O. Weber 51 6 0 13 Nov 2020
Domain-Level Explainability -- A Challenge for Creating Trust in Superhuman AI Strategies Jonas Andrulis Ole Meyer Grégory Schott Samuel Weinbach V. Gruhn 39 4 0 12 Nov 2020
Generalized Constraints as A New Mathematical Problem in Artificial Intelligence: A Review and Perspective Bao-Gang Hu Hanbing Qu AI4CE 105 1 0 12 Nov 2020
Interpretable collaborative data analysis on distributed data A. Imakura Hiroaki Inaba Yukihiko Okada Tetsuya Sakurai FedML 38 26 0 09 Nov 2020
FairLens: Auditing Black-box Clinical Decision Support Systems Cecilia Panigutti Alan Perotti Andre' Panisson P. Bajardi D. Pedreschi 88 70 0 08 Nov 2020
Feature Removal Is a Unifying Principle for Model Explanation Methods Ian Covert Scott M. Lundberg Su-In Lee FAtt 138 33 0 06 Nov 2020
Explaining Differences in Classes of Discrete Sequences Samaneh Saadat G. Sukthankar 8 3 0 06 Nov 2020
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition Meike Nauta Annemarie Jutte Jesper C. Provoost C. Seifert FAtt 109 65 0 05 Nov 2020
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions Kasun Amarasinghe Kit Rodolfa Hemank Lamba Rayid Ghani ELM XAI 187 53 0 27 Oct 2020
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles Rory Mitchell E. Frank G. Holmes 45 57 0 27 Oct 2020
Abduction and Argumentation for Explainable Machine Learning: A Position Survey A. Kakas Loizos Michael 29 11 0 24 Oct 2020
An Analysis of LIME for Text Data Dina Mardaoui Damien Garreau FAtt 195 45 0 23 Oct 2020
Model Interpretability through the Lens of Computational Complexity Pablo Barceló Mikaël Monet Jorge A. Pérez Bernardo Subercaseaux 212 98 0 23 Oct 2020
Towards falsifiable interpretability research Matthew L. Leavitt Ari S. Morcos AAML AI4CE 84 68 0 22 Oct 2020
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games Yunqiu Xu Meng Fang Ling-Hao Chen Yali Du Qiufeng Wang Chengqi Zhang OffRL 82 44 0 22 Oct 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 93 88 0 21 Oct 2020
Explaining black-box text classifiers for disease-treatment information extraction M. Moradi Matthias Samwald 92 2 0 21 Oct 2020
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 178 177 0 20 Oct 2020
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images Pablo Messina Pablo Pino Denis Parra Alvaro Soto Cecilia Besa S. Uribe Marcelo andía C. Tejos Claudia Prieto Daniel Capurro MedIm 125 64 0 20 Oct 2020
ERIC: Extracting Relations Inferred from Convolutions Joe Townsend Theodoros Kasioumis Hiroya Inakoshi NAI FAtt 82 16 0 19 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 114 405 0 19 Oct 2020
Understanding Information Processing in Human Brain by Interpreting Machine Learning Models Ilya Kuzovkin HAI 24 2 0 17 Oct 2020
A general approach to compute the relevance of middle-level input features Andrea Apicella Salvatore Giugliano Francesco Isgrò R. Prevete 49 6 0 16 Oct 2020
Explaining Neural Network Predictions for Functional Data Using Principal Component Analysis and Feature Importance Katherine Goode Daniel Ries J. Zollweg 31 3 0 15 Oct 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 328 450 0 15 Oct 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines A. Konstantinov Lev V. Utkin FedML AI4CE 60 153 0 14 Oct 2020
Robust Fairness under Covariate Shift Ashkan Rezaei Anqi Liu Omid Memarrast Brian Ziebart TTA OOD 135 86 0 11 Oct 2020
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations Andrea Ferrario M. Loi 62 5 0 09 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 70 172 0 08 Oct 2020
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification Florentin Flambeau Jiechieu Kameni Norbert Tsopzé XAI FAtt MedIm 26 1 0 08 Oct 2020
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision Max Glockner Ivan Habernal Iryna Gurevych LRM 95 26 0 07 Oct 2020
Efficient computation of contrastive explanations André Artelt Barbara Hammer 41 9 0 06 Oct 2020