Local Rule-Based Explanations of Black Box Decision Systems

28 May 2018

Papers citing "Local Rule-Based Explanations of Black Box Decision Systems"

50 / 192 papers shown

Title
XPROAX-Local explanations for text classification with progressive neighborhood approximation Yi Cai Arthur Zimek Eirini Ntoutsi 25 5 0 30 Sep 2021
Exploring The Role of Local and Global Explanations in Recommender Systems Marissa Radensky Doug Downey Kyle Lo Z. Popović Daniel S. Weld University of Washington LRM 13 20 0 27 Sep 2021
Counterfactual Instances Explain Little Adam White Artur Garcez CML 27 5 0 20 Sep 2021
An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets Jun Yuan O. Nov E. Bertini 20 10 0 19 Sep 2021
Beyond Average Performance -- exploring regions of deviating performance for black box classification models Luís Torgo Paulo Azevedo Inês Areosa 11 2 0 16 Sep 2021
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML CML HAI 16 21 0 12 Sep 2021
Interpretable Run-Time Prediction and Planning in Co-Robotic Environments Rahul Peddi N. Bezzo 20 2 0 08 Sep 2021
Synthesizing Pareto-Optimal Interpretations for Black-Box Models Hazem Torfah Shetal Shah Supratik Chakraborty S. Akshay S. Seshia 22 6 0 16 Aug 2021
Logic Explained Networks Gabriele Ciravegna Pietro Barbiero Francesco Giannini Marco Gori Pietro Lió Marco Maggini S. Melacci 35 69 0 11 Aug 2021
Desiderata for Explainable AI in statistical production systems of the European Central Bank Carlos Navarro Georgios Kanellos Thomas Gottron 12 9 0 18 Jul 2021
Understanding surrogate explanations: the interplay between complexity, fidelity and coverage Rafael Poyiadzi X. Renard Thibault Laugel Raúl Santos-Rodríguez Marcin Detyniecki 11 6 0 09 Jul 2021
A Review of Explainable Artificial Intelligence in Manufacturing G. Sofianidis Jože M. Rožanec Dunja Mladenić D. Kyriazis 17 17 0 05 Jul 2021
Productivity, Portability, Performance: Data-Centric Python Yiheng Wang Yao Zhang Yanzhang Wang Yan Wan Jiao Wang Zhongyuan Wu Yuhao Yang Bowen She 52 94 0 01 Jul 2021
Explanation-Guided Diagnosis of Machine Learning Evasion Attacks Abderrahmen Amich Birhanu Eshete AAML 17 10 0 30 Jun 2021
On Locality of Local Explanation Models Sahra Ghalebikesabi Lucile Ter-Minassian Karla Diaz-Ordaz Chris Holmes FedML FAtt 18 39 0 24 Jun 2021
Multivariate Data Explanation by Jumping Emerging Patterns Visualization Mário Popolin Neto F. Paulovich 24 7 0 21 Jun 2021
An Empirical Investigation into Deep and Shallow Rule Learning Florian Beck Johannes Furnkranz NAI 18 7 0 18 Jun 2021
A Framework for Evaluating Post Hoc Feature-Additive Explainers Zachariah Carmichael Walter J. Scheirer FAtt 46 4 0 15 Jun 2021
Counterfactual Explanations as Interventions in Latent Space Riccardo Crupi Alessandro Castelnovo D. Regoli Beatriz San Miguel González CML 8 23 0 14 Jun 2021
Certification of embedded systems based on Machine Learning: A survey Guillaume Vidot Christophe Gabreau I. Ober Iulian Ober 11 12 0 14 Jun 2021
Entropy-based Logic Explanations of Neural Networks Pietro Barbiero Gabriele Ciravegna Francesco Giannini Pietro Lió Marco Gori S. Melacci FAtt XAI 25 78 0 12 Jun 2021
On the overlooked issue of defining explanation objectives for local-surrogate explainers Rafael Poyiadzi X. Renard Thibault Laugel Raúl Santos-Rodríguez Marcin Detyniecki 16 6 0 10 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer Sina Mohseni Haotao Wang Zhiding Yu Chaowei Xiao Zhangyang Wang J. Yadawa 21 31 0 09 Jun 2021
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models Sahil Verma Keegan E. Hines John P. Dickerson 22 23 0 07 Jun 2021
An exact counterfactual-example-based approach to tree-ensemble models interpretability P. Blanchart 14 4 0 31 May 2021
Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN? J. Ren Zhanpeng Zhou Qirui Chen Quanshi Zhang FAtt TDI 33 8 0 22 May 2021
Rule Generation for Classification: Scalability, Interpretability, and Fairness Tabea E. Rober Adia C. Lumadjeng M. Akyuz cS. .Ilker Birbil 19 2 0 21 Apr 2021
Conclusive Local Interpretation Rules for Random Forests Ioannis Mollas Nick Bassiliades Grigorios Tsoumakas FaML FAtt 29 17 0 13 Apr 2021
Individual Explanations in Machine Learning Models: A Survey for Practitioners Alfredo Carrillo Luis F. Cantú Alejandro Noriega FaML 16 15 0 09 Apr 2021
Shapley Explanation Networks Rui Wang Xiaoqian Wang David I. Inouye TDI FAtt 19 44 0 06 Apr 2021
Semantic XAI for contextualized demand forecasting explanations Jože M. Rožanec Dunja Mladenić 30 4 0 01 Apr 2021
Deep Learning for Android Malware Defenses: a Systematic Literature Review Yue Liu C. Tantithamthavorn Li Li Yepang Liu AAML 30 77 0 09 Mar 2021
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications Yu-Liang Chou Catarina Moreira P. Bruza Chun Ouyang Joaquim A. Jorge CML 47 176 0 07 Mar 2021
Evaluating Robustness of Counterfactual Explanations André Artelt Valerie Vaquet Riza Velioglu Fabian Hinder Johannes Brinkrolf M. Schilling Barbara Hammer 11 46 0 03 Mar 2021
Visualizing Rule Sets: Exploration and Validation of a Design Space Jun Yuan O. Nov E. Bertini 23 1 0 01 Mar 2021
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques Mark T. Keane Eoin M. Kenny Eoin Delaney Barry Smyth CML 24 146 0 26 Feb 2021
Benchmarking and Survey of Explanation Methods for Black Box Models F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 33 220 0 25 Feb 2021
SQAPlanner: Generating Data-Informed Software Quality Improvement Plans Dilini Sewwandi Rajapaksha C. Tantithamthavorn Jirayus Jiarpakdee Christoph Bergmeir J. Grundy Wray L. Buntine 17 34 0 19 Feb 2021
Bandits for Learning to Explain from Explanations Freya Behrens Stefano Teso Davide Mottin FAtt 11 1 0 07 Feb 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 115 142 0 05 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
Explaining Black-box Models for Biomedical Text Classification M. Moradi Matthias Samwald 31 21 0 20 Dec 2020
Why model why? Assessing the strengths and limitations of LIME Jurgen Dieber S. Kirrane FAtt 17 97 0 30 Nov 2020
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 23 751 0 16 Nov 2020
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 19 99 0 10 Nov 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 24 162 0 20 Oct 2020
A general approach to compute the relevance of middle-level input features Andrea Apicella Salvatore Giugliano Francesco Isgrò R. Prevete 12 6 0 16 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 14 172 0 08 Oct 2020
Visualizing Color-wise Saliency of Black-Box Image Classification Models Yuhki Hatakeyama Hiroki Sakuma Yoshinori Konishi Kohei Suenaga FAtt 14 3 0 06 Oct 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 33 62 0 11 Sep 2020