Benchmarking and Survey of Explanation Methods for Black Box Models

25 February 2021

Papers citing "Benchmarking and Survey of Explanation Methods for Black Box Models"

50 / 105 papers shown

Title
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models Célia Wafa Ayad Thomas Bonnier Benjamin Bosch Sonali Parbhoo Jesse Read FAtt XAI 120 0 0 11 Feb 2025
Coherent Local Explanations for Mathematical Optimization Daan Otto Jannis Kurtz S. Ilker Birbil 77 0 0 07 Feb 2025
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients Evandro S. Ortigossa Fábio F. Dias Brian Barr Claudio T. Silva L. G. Nonato FAtt 91 3 0 25 Apr 2024
Generating Likely Counterfactuals Using Sum-Product Networks Jiri Nemecek Tomás Pevný Georgios Korpas TPM 83 0 0 25 Jan 2024
Distributional Counterfactual Explanations With Optimal Transport Lei You Lele Cao Mattias Nilsson Bo Zhao Lei Lei OT OffRL 83 1 0 23 Jan 2024
Opti-CAM: Optimizing saliency maps for interpretability Hanwei Zhang Felipe Torres R. Sicre Yannis Avrithis Stéphane Ayache 67 24 0 17 Jan 2023
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges Mario Alfonso Prado-Romero Bardh Prenkaj Giovanni Stilo F. Giannotti CML 83 32 0 21 Oct 2022
Global Counterfactual Explanations: Investigations, Implementations and Improvements Dan Ley Saumitra Mishra Daniele Magazzeni LRM 68 12 0 14 Apr 2022
ProtoTEx: Explaining Model Decisions with Prototype Tensors Anubrata Das Chitrank Gupta Venelin Kovatchev Matthew Lease Junjie Li 50 27 0 11 Apr 2022
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes Jonathan Donnelly A. Barnett Chaofan Chen 3DH 69 128 0 29 Nov 2021
Let the CAT out of the bag: Contrastive Attributed explanations for Text Saneem A. Chemmengath A. Azad Ronny Luss Amit Dhurandhar FAtt 57 10 0 16 Sep 2021
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs V. Kamakshi Uday Gupta N. C. Krishnan 40 18 0 31 Aug 2021
Interpretable Compositional Convolutional Neural Networks Wen Shen Zhihua Wei Shikun Huang Binbin Zhang Jiaqi Fan Ping Zhao Quanshi Zhang FAtt 27 34 0 09 Jul 2021
Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang Yossi Gandelsman Michal Yarom Yoav Wald G. Elidan ... William T. Freeman Phillip Isola Amir Globerson Michal Irani Inbar Mosseri GAN 85 152 0 27 Apr 2021
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey Thomas Rojat Raphael Puget David Filliat Javier Del Ser R. Gelin Natalia Díaz Rodríguez XAI AI4TS 68 131 0 02 Apr 2021
ECINN: Efficient Counterfactuals from Invertible Neural Networks Frederik Hvilshoj Alexandros Iosifidis Ira Assent BDL 36 26 0 25 Mar 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 137 145 0 05 Feb 2021
GLocalX -- From Local to Global Explanations of Black Box AI Models Mattia Setzu Riccardo Guidotti A. Monreale Franco Turini D. Pedreschi F. Giannotti 41 119 0 19 Jan 2021
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models Tongshuang Wu Marco Tulio Ribeiro Jeffrey Heer Daniel S. Weld 85 246 0 01 Jan 2021
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text Nishtha Madaan Inkit Padhi Naveen Panwar Diptikalyan Saha CML 63 99 0 08 Dec 2020
Neural Prototype Trees for Interpretable Fine-grained Image Recognition Meike Nauta Ron van Bree C. Seifert 125 263 0 03 Dec 2020
Debugging Tests for Model Explanations Julius Adebayo M. Muelly Ilaria Liccardi Been Kim FAtt 56 179 0 10 Nov 2020
Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis Zhengxuan Wu Desmond C. Ong 39 79 0 15 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 54 172 0 08 Oct 2020
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning Eoin M. Kenny Mark T. Keane 50 100 0 10 Sep 2020
Eigen-CAM: Class Activation Map using Principal Components Mohammed Bany Muhammad M. Yeasin 40 339 0 01 Aug 2020
XGNN: Towards Model-Level Explanations of Graph Neural Networks Haonan Yuan Jiliang Tang Xia Hu Shuiwang Ji 68 393 0 03 Jun 2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? Peter Hase Joey Tianyi Zhou FAtt 70 301 0 04 May 2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 62 412 0 29 Apr 2020
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation Sheng Shi Xinfeng Zhang Wei Fan FAtt 32 28 0 18 Feb 2020
BRPO: Batch Residual Policy Optimization Kentaro Kanamori Yinlam Chow Takuya Takagi Hiroki Arimura Honglak Lee Ken Kobayashi Craig Boutilier OffRL 180 45 0 08 Feb 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space Riccardo Guidotti A. Monreale Stan Matwin D. Pedreschi FAtt 75 67 0 27 Jan 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh Dawei Yin Yi-Ju Chang FAtt 64 348 0 17 Jan 2020
On the computation of counterfactual explanations -- A survey André Artelt Barbara Hammer LRM 54 50 0 15 Nov 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 110 208 0 27 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 97 6,211 0 22 Oct 2019
Learning Model-Agnostic Counterfactual Explanations for Tabular Data Martin Pawelczyk Johannes Haug Klaus Broelemann Gjergji Kasneci OOD CML 58 203 0 21 Oct 2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 54 414 0 18 Oct 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan O. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 182 302 0 17 Oct 2019
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models Benjamin Hoover Hendrik Strobelt Sebastian Gehrmann 25 85 0 11 Oct 2019
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks Mehdi Neshat Zifan Wang Bradley Alexander Fan Yang Zijian Zhang Sirui Ding Markus Wagner Xia Hu FAtt 80 1,056 0 03 Oct 2019
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers Robert Schwarzenberg Marc Hübner David Harbecke Christoph Alt Leonhard Hennig FAtt GNN 40 69 0 24 Sep 2019
FACE: Feasible and Actionable Counterfactual Explanations Rafael Poyiadzi Kacper Sokol Raúl Santos-Rodríguez T. D. Bie Peter A. Flach 66 368 0 20 Sep 2019
InterpretML: A Unified Framework for Machine Learning Interpretability Harsha Nori Samuel Jenkins Paul Koch R. Caruana AI4CE 109 486 0 19 Sep 2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 35 391 0 06 Sep 2019
ALIME: Autoencoder Based Approach for Local Interpretability Sharath M. Shankaranarayana D. Runje FAtt 35 103 0 04 Sep 2019
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting Ana Lucic H. Haned Maarten de Rijke 54 63 0 17 Jul 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 89 1,427 0 17 Jul 2019
Explaining Classifiers with Causal Concept Effect (CaCE) Yash Goyal Amir Feder Uri Shalit Been Kim CML 69 175 0 16 Jul 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 49 380 0 03 Jul 2019