Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring

4 September 2020

Papers citing "Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring"

32 / 32 papers shown

Title
A systematic review and taxonomy of explanations in decision support and recommender systems Ingrid Nunes Dietmar Jannach XAI 38 329 0 15 Jun 2020
Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction Farbod Taymouri M. Rosa S. Erfani Z. Bozorgi I. Verenich GAN AAML 27 86 0 25 Mar 2020
AI Trust in business processes: The need for process-aware explanations Steve T. K. Jan Vatche Isahagian Vinod Muthusamy 26 23 0 21 Jan 2020
Exploring Interpretability for Predictive Process Analytics Renuka Sindhgatta Chun Ouyang Catarina Moreira 14 2 0 22 Dec 2019
Explaining Explanations in AI Brent Mittelstadt Chris Russell Sandra Wachter XAI 83 664 0 04 Nov 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 46 128 0 08 Oct 2018
Stakeholders in Explainable AI Alun D. Preece Daniel Harborne Dave Braines Richard J. Tomsett Supriyo Chakraborty 35 154 0 29 Sep 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 60 524 0 21 Jun 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 101 164 0 20 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 136 1,164 0 19 Jun 2018
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP) Linwei Hu Jie Chen V. Nair Agus Sudjianto FAtt 40 63 0 02 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 75 1,849 0 31 May 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 118 436 0 28 May 2018
Predictive Process Monitoring Methods: Which One Suits Me Best? Chiara Di Francescomarino Chiara Ghidini F. Maggi Fredrik P. Milani 31 146 0 06 Apr 2018
On Cognitive Preferences and the Plausibility of Rule-based Models Johannes Furnkranz Tomáš Kliegr Heiko Paulheim LRM 45 69 0 04 Mar 2018
Consistent Individualized Feature Attribution for Tree Ensembles Scott M. Lundberg G. Erion Su-In Lee FAtt TDI 55 1,379 0 12 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 90 3,922 0 06 Feb 2018
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers Fred Hohman Minsuk Kahng Robert S. Pienta Duen Horng Chau OOD HAI 68 538 0 21 Jan 2018
Distilling a Neural Network Into a Soft Decision Tree Nicholas Frosst Geoffrey E. Hinton 245 635 0 27 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 227 4,229 0 22 Jun 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 192 2,215 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 651 21,613 0 22 May 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 142 5,920 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 354 3,742 0 28 Feb 2017
Predicting Process Behaviour using Deep Learning Joerg Evermann Jana-Rebecca Rehse Peter Fettke 59 353 0 14 Dec 2016
Predictive Business Process Monitoring with LSTM Neural Networks Niek Tax I. Verenich M. Rosa Marlon Dumas 36 444 0 07 Dec 2016
Grad-CAM: Why did you say that? Ramprasaath R. Selvaraju Abhishek Das Ramakrishna Vedantam Michael Cogswell Devi Parikh Dhruv Batra FAtt 50 469 0 22 Nov 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 134 3,672 0 10 Jun 2016
Time and Activity Sequence Prediction of Business Process Instances Mirko Polato A. Sperduti Andrea Burattin M. Leoni AI4TS 22 144 0 24 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 681 16,828 0 16 Feb 2016
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 191 4,653 0 21 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 207 7,252 0 20 Dec 2013