Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges

19 October 2020

Papers citing "Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges"

50 / 71 papers shown

Title
Interpretable DNFs Martin C. Cooper Imane Bousdira Clément Carbonnel FAtt AI4CE 27 0 0 27 May 2025
Verification and Validation for Trustworthy Scientific Machine Learning John D. Jakeman Lorena A. Barba J. Martins Thomas O'Leary-Roseberry AI4CE 101 0 0 21 Feb 2025
Surrogate Modeling for Explainable Predictive Time Series Corrections Alfredo Lopez Florian Sobieczky AI4TS 81 0 0 17 Jan 2025
Genetic Programming for Explainable Manifold Learning Ben Cravens Andrew Lensen Paula Maddigan Bing Xue 74 1 0 21 Mar 2024
Finding Optimal Diverse Feature Sets with Alternative Feature Selection Jakob Bach 146 1 0 21 Jul 2023
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 116 1 0 17 Feb 2023
MeLIME: Meaningful Local Explanation for Machine Learning Models T. Botari Frederik Hvilshoj Rafael Izbicki A. Carvalho AAML FAtt 53 16 0 12 Sep 2020
Predictive and Causal Implications of using Shapley Value for Model Interpretation Sisi Ma R. Tourani FAtt TDI 31 34 0 12 Aug 2020
On Feature Relevance Uncertainty: A Monte Carlo Dropout Sampling Approach Kai Fabi Jonas Schneider BDL 24 4 0 04 Aug 2020
audioLIME: Listenable Explanations Using Source Separation Verena Haunschmid Ethan Manilow Gerhard Widmer FAtt 32 32 0 02 Aug 2020
Relative Feature Importance Gunnar Konig Christoph Molnar B. Bischl Moritz Grosse-Wentrup 49 49 0 16 Jul 2020
Efficient nonparametric statistical inference on population feature importance using Shapley values B. Williamson Jean Feng FAtt 39 72 0 16 Jun 2020
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms Giorgio Visani Enrico Bagli F. Chesani FAtt 44 60 0 10 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 64 270 0 29 May 2020
Multi-Objective Counterfactual Explanations Susanne Dandl Christoph Molnar Martin Binder B. Bischl 57 257 0 23 Apr 2020
Problems with Shapley-value-based explanations as feature importance measures Indra Elizabeth Kumar Suresh Venkatasubramanian C. Scheidegger Sorelle A. Friedler TDI FAtt 76 362 0 25 Feb 2020
Causality for Machine Learning Bernhard Schölkopf CML AI4CE LRM 82 461 0 24 Nov 2019
A study of data and label shift in the LIME framework Amir Hossein Akhavan Rahnama Henrik Bostrom FAtt 15 21 0 31 Oct 2019
Feature relevance quantification in explainable AI: A causal problem Dominik Janzing Lenon Minorics Patrick Blobaum FAtt CML 57 280 0 29 Oct 2019
Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph Johannes Rabold Hannah Deininger M. Siebers Ute Schmid FAtt 49 57 0 04 Oct 2019
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 63 442 0 26 Sep 2019
InterpretML: A Unified Framework for Machine Learning Interpretability Harsha Nori Samuel Jenkins Paul Koch R. Caruana AI4CE 138 487 0 19 Sep 2019
ALIME: Autoencoder Based Approach for Local Interpretability Sharath M. Shankaranarayana D. Runje FAtt 46 103 0 04 Sep 2019
The many Shapley values for model explanation Mukund Sundararajan A. Najmi TDI FAtt 58 632 0 22 Aug 2019
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 48 198 0 22 Jul 2019
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 79 492 0 09 Jul 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 106 1,015 0 19 May 2019
Explainability in Human-Agent Systems A. Rosenfeld A. Richardson XAI 54 204 0 17 Apr 2019
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability Christoph Molnar Giuseppe Casalicchio B. Bischl FAtt 41 60 0 08 Apr 2019
Assessing the Local Interpretability of Machine Learning Models Dylan Slack Sorelle A. Friedler C. Scheidegger Chitradeep Dutta Roy FAtt 43 71 0 09 Feb 2019
Testing Conditional Independence in Supervised Learning Algorithms David S. Watson Marvin N. Wright CML 47 53 0 28 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 179 1,436 0 14 Jan 2019
Metrics for Explainable AI: Challenges and Prospects R. Hoffman Shane T. Mueller Gary Klein Jordan Litman XAI 74 727 0 11 Dec 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 123 1,963 0 08 Oct 2018
Stakeholders in Explainable AI Alun D. Preece Daniel Harborne Dave Braines Richard J. Tomsett Supriyo Chakraborty 38 155 0 29 Sep 2018
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 105 548 0 18 Sep 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 77 1,090 0 31 Jul 2018
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 62 215 0 17 Jul 2018
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP) Linwei Hu Jie Chen V. Nair Agus Sudjianto FAtt 51 63 0 02 Jun 2018
A Simple and Effective Model-Based Variable Importance Measure Brandon M. Greenwell Bradley C. Boehmke Andrew J. McCarthy FAtt TDI 34 228 0 12 May 2018
Visualizing the Feature Importance for Black Box Models Giuseppe Casalicchio Christoph Molnar B. Bischl FAtt 38 183 0 18 Apr 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 84 698 0 21 Feb 2018
Consistent Individualized Feature Attribution for Tree Ensembles Scott M. Lundberg G. Erion Su-In Lee FAtt TDI 59 1,392 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 122 3,938 0 06 Feb 2018
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning Sina Mohseni Jeremy E. Block Eric D. Ragan FAtt XAI 76 61 0 16 Jan 2018
Distilling a Neural Network Into a Soft Decision Tree Nicholas Frosst Geoffrey E. Hinton 381 636 0 27 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 98 2,348 0 01 Nov 2017
Interpretable Convolutional Neural Networks Quanshi Zhang Ying Nian Wu Song-Chun Zhu FAtt 64 780 0 02 Oct 2017
Fast calibrated additive quantile regression Matteo Fasiolo S. Wood Margaux Zaffran Raphael Nedellec Y. Goude 36 197 0 11 Jul 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 239 4,249 0 22 Jun 2017