Debugging Tests for Model Explanations

10 November 2020

Papers citing "Debugging Tests for Model Explanations"

48 / 48 papers shown

Title
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics Lukas Klein Carsten T. Lüth U. Schlegel Till J. Bungert Mennatallah El-Assady Paul F. Jäger XAI ELM 42 2 0 03 Jan 2025
Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse Seung Hyun Cheon Anneke Wernerfelt Sorelle A. Friedler Berk Ustun FaML FAtt 45 0 0 29 Oct 2024
Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector Xianren Zhang Dongwon Lee Suhang Wang VLM FAtt 50 3 0 27 Jul 2024
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks Sarah Jabbour Gregory Kondas Ella Kazerooni Michael Sjoding David Fouhey Jenna Wiens FAtt DiffM 47 1 0 19 Jul 2024
What Sketch Explainability Really Means for Downstream Tasks Hmrishav Bandyopadhyay Pinaki Nath Chowdhury A. Bhunia Aneeshan Sain Tao Xiang Yi-Zhe Song 30 4 0 14 Mar 2024
Black-Box Access is Insufficient for Rigorous AI Audits Stephen Casper Carson Ezell Charlotte Siegmann Noam Kolt Taylor Lynn Curtis ... Michael Gerovitch David Bau Max Tegmark David M. Krueger Dylan Hadfield-Menell AAML 34 78 0 25 Jan 2024
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training Rui Xu Wenkang Qin Peixiang Huang Hao Wang Lin Luo FAtt AAML 28 2 0 09 Nov 2023
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods Robin Hesse Simone Schaub-Meyer Stefan Roth AAML 37 32 0 11 Aug 2023
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition Xiao-lan Wu P. Bell A. Rajan 19 5 0 29 May 2023
UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs V. V. Ramaswamy Sunnie S. Y. Kim Ruth C. Fong Olga Russakovsky 35 0 0 27 Mar 2023
Perceptual Pat: A Virtual Human System for Iterative Visualization Design Sungbok Shin San Hong Niklas Elmqvist 14 8 0 12 Mar 2023
Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook Teresa Datta John P. Dickerson 34 10 0 10 Mar 2023
Tracr: Compiled Transformers as a Laboratory for Interpretability David Lindner János Kramár Sebastian Farquhar Matthew Rahtz Tom McGrath Vladimir Mikulik 29 72 0 12 Jan 2023
Robust Explanation Constraints for Neural Networks Matthew Wicker Juyeon Heo Luca Costabello Adrian Weller FAtt 29 18 0 16 Dec 2022
Tensions Between the Proxies of Human Values in AI Teresa Datta D. Nissani Max Cembalest Akash Khanna Haley Massa John P. Dickerson 34 2 0 14 Dec 2022
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models Dong-Ho Lee Akshen Kadakia Brihi Joshi Aaron Chan Ziyi Liu ... Takashi Shibuya Ryosuke Mitani Toshiyuki Sekiya Jay Pujara Xiang Ren LRM 40 9 0 30 Oct 2022
Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection Tulika Bose Nikolaos Aletras Irina Illina Dominique Fohr 19 0 0 18 Sep 2022
Leveraging Explanations in Interactive Machine Learning: An Overview Stefano Teso Öznur Alkan Wolfgang Stammer Elizabeth M. Daly XAI FAtt LRM 26 62 0 29 Jul 2022
Unit Testing for Concepts in Neural Networks Charles Lovering Ellie Pavlick 25 28 0 28 Jul 2022
An Interpretability Evaluation Benchmark for Pre-trained Language Models Ya-Ming Shen Lijie Wang Ying-Cong Chen Xinyan Xiao Jing Liu Hua Wu 37 4 0 28 Jul 2022
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy Giang Nguyen Mohammad Reza Taesiri Anh Totti Nguyen 30 42 0 26 Jul 2022
Auditing Visualizations: Transparency Methods Struggle to Detect Anomalous Behavior Jean-Stanislas Denain Jacob Steinhardt AAML 42 7 0 27 Jun 2022
Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations Teodor Chiaburu F. Biessmann Frank Haußer 35 2 0 15 Jun 2022
Use-Case-Grounded Simulations for Explanation Evaluation Valerie Chen Nari Johnson Nicholay Topin Gregory Plumb Ameet Talwalkar FAtt ELM 22 24 0 05 Jun 2022
The Solvability of Interpretability Evaluation Metrics Yilun Zhou J. Shah 70 8 0 18 May 2022
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 30 11 0 13 May 2022
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making Max Schemmer Patrick Hemmer Maximilian Nitsche Niklas Kühl Michael Vossing 24 56 0 10 May 2022
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset Leon Sixt M. Schuessler Oana-Iuliana Popescu Philipp Weiß Tim Landgraf FAtt 29 14 0 25 Apr 2022
Dynamically Refined Regularization for Improving Cross-corpora Hate Speech Detection Tulika Bose Nikolaos Aletras Irina Illina Dominique Fohr 45 5 0 23 Mar 2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 21 168 0 14 Feb 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo S. Feizi FAtt 20 80 0 29 Nov 2021
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining Andreas Madsen Nicholas Meade Vaibhav Adlakha Siva Reddy 111 35 0 15 Oct 2021
Jointly Attacking Graph Neural Network and its Explanations Wenqi Fan Wei Jin Xiaorui Liu Han Xu Xianfeng Tang Suhang Wang Qing Li Jiliang Tang Jianping Wang Charu C. Aggarwal AAML 42 28 0 07 Aug 2021
Characterizing the risk of fairwashing Ulrich Aïvodji Hiromi Arai Sébastien Gambs Satoshi Hara 23 27 0 14 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das K. Kenthapadi AAML 11 21 0 08 Jun 2021
3DB: A Framework for Debugging Computer Vision Models Guillaume Leclerc Hadi Salman Andrew Ilyas Sai H. Vemprala Logan Engstrom ... Pengchuan Zhang Shibani Santurkar Greg Yang Ashish Kapoor A. Madry 40 40 0 07 Jun 2021
Sanity Simulations for Saliency Methods Joon Sik Kim Gregory Plumb Ameet Talwalkar FAtt 38 17 0 13 May 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar A. Madry FAtt 22 88 0 11 May 2021
Explanation-Based Human Debugging of NLP Models: A Survey Piyawat Lertvittayakumjorn Francesca Toni LRM 42 79 0 30 Apr 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 33 132 0 27 Apr 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 23 57 0 25 Feb 2021
Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antorán Umang Bhatt T. Adel Adrian Weller José Miguel Hernández-Lobato UQCV BDL 40 111 0 11 Jun 2020
The Grammar of Interactive Explanatory Model Analysis Hubert Baniecki Dariusz Parzych P. Biecek 21 44 0 01 May 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 21 150 0 16 Mar 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,690 0 28 Feb 2017