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
What does LIME really see in images? Damien Garreau Dina Mardaoui FAtt 19 38 0 11 Feb 2021
EUCA: the End-User-Centered Explainable AI Framework Weina Jin Jianyu Fan D. Gromala Philippe Pasquier Ghassan Hamarneh 40 24 0 04 Feb 2021
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens Maia L. Jacobs Jeffrey He Melanie F. Pradier Barbara D. Lam Andrew C Ahn T. McCoy R. Perlis Finale Doshi-Velez Krzysztof Z. Gajos 49 145 0 01 Feb 2021
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 42 170 0 13 Jan 2021
Weighted defeasible knowledge bases and a multipreference semantics for a deep neural network model Laura Giordano Daniele Theseider Dupré 36 35 0 24 Dec 2020
Explaining Black-box Models for Biomedical Text Classification M. Moradi Matthias Samwald 39 21 0 20 Dec 2020
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to Theory Nazanin Fouladgar Kary Främling XAI 15 4 0 17 Dec 2020
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges Kashif Ahmad Majdi Maabreh M. Ghaly Khalil Khan Junaid Qadir Ala I. Al-Fuqaha 27 142 0 14 Dec 2020
Explanation from Specification Harish Naik Gyorgy Turán XAI 27 0 0 13 Dec 2020
Demystifying Deep Neural Networks Through Interpretation: A Survey Giang Dao Minwoo Lee FaML FAtt 16 1 0 13 Dec 2020
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 24 18 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 48 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 14 1 0 08 Dec 2020
Neural Prototype Trees for Interpretable Fine-grained Image Recognition Meike Nauta Ron van Bree C. Seifert 80 262 0 03 Dec 2020
Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information F. Simini Gianni Barlacchi Massimilano Luca Luca Pappalardo HAI 21 174 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 24 88 0 30 Nov 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 39 241 0 21 Nov 2020
Interpretable collaborative data analysis on distributed data A. Imakura Hiroaki Inaba Yukihiko Okada Tetsuya Sakurai FedML 19 26 0 09 Nov 2020
Feature Removal Is a Unifying Principle for Model Explanation Methods Ian Covert Scott M. Lundberg Su-In Lee FAtt 33 33 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 22 65 0 05 Nov 2020
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles Rory Mitchell E. Frank G. Holmes 14 55 0 27 Oct 2020
Abduction and Argumentation for Explainable Machine Learning: A Position Survey A. Kakas Loizos Michael 9 11 0 24 Oct 2020
Model Interpretability through the Lens of Computational Complexity Pablo Barceló Mikaël Monet Jorge A. Pérez Bernardo Subercaseaux 126 94 0 23 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 25 44 0 22 Oct 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 24 85 0 21 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 36 62 0 20 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 20 397 0 19 Oct 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines A. Konstantinov Lev V. Utkin FedML AI4CE 10 138 0 14 Oct 2020
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations Andrea Ferrario M. Loi 25 5 0 09 Oct 2020
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision Max Glockner Ivan Habernal Iryna Gurevych LRM 27 25 0 07 Oct 2020
A Survey on Explainability in Machine Reading Comprehension Mokanarangan Thayaparan Marco Valentino André Freitas FaML 12 50 0 01 Oct 2020
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach Rémi Eyraud Stéphane Ayache 24 16 0 28 Sep 2020
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing Nijat Mehdiyev Peter Fettke 14 11 0 22 Sep 2020
Machine Guides, Human Supervises: Interactive Learning with Global Explanations Teodora Popordanoska Mohit Kumar Stefano Teso 21 21 0 21 Sep 2020
Explainable boosted linear regression for time series forecasting Igor Ilic Berk Görgülü Mucahit Cevik M. Baydogan AI4TS 8 62 0 18 Sep 2020
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting Ozan Ozyegen Igor Ilic Mucahit Cevik FAtt AI4TS 16 2 0 18 Sep 2020
Better Model Selection with a new Definition of Feature Importance Fan Fang Carmine Ventre Lingbo Li Leslie Kanthan Fan Wu Michail Basios FAtt 40 5 0 16 Sep 2020
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning Eoin M. Kenny Mark T. Keane 12 99 0 10 Sep 2020
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring Nijat Mehdiyev Peter Fettke AI4TS 25 55 0 04 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 30 51 0 03 Sep 2020
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Sasha Rubin Thomas Gerspacher Martin C. Cooper Alexey Ignatiev Nina Narodytska FAtt 27 59 0 13 Aug 2020
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs Ruigang Fu Qingyong Hu Xiaohu Dong Yulan Guo Yinghui Gao Biao Li FAtt 24 266 0 05 Aug 2020
Explainable Predictive Process Monitoring Musabir Musabayli F. Maggi Williams Rizzi Josep Carmona Chiara Di Francescomarino 14 60 0 04 Aug 2020
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance Mattia Carletti M. Terzi Gian Antonio Susto 36 42 0 21 Jul 2020
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates Roel Henckaerts Katrien Antonio Marie-Pier Côté 23 3 0 14 Jul 2020
Editable AI: Mixed Human-AI Authoring of Code Patterns Kartik Chugh Andrea Y. Solis Thomas D. Latoza 9 3 0 12 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 30 625 0 01 Jul 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 27 19 0 26 Jun 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 30 73 0 24 Jun 2020
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction Esther Puyol-Antón Chong Chen J. Clough B. Ruijsink B. Sidhu ... M. Elliott Vishal S. Mehta Daniel Rueckert C. Rinaldi A. King 19 32 0 24 Jun 2020