Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

6 November 2019

Himabindu Lakkaraju

Papers citing "Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods"

38 / 138 papers shown

Title
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations Bodhisattwa Prasad Majumder Oana-Maria Camburu Thomas Lukasiewicz Julian McAuley 25 35 0 25 Jun 2021
What will it take to generate fairness-preserving explanations? Jessica Dai Sohini Upadhyay Stephen H. Bach Himabindu Lakkaraju FAtt FaML 15 14 0 24 Jun 2021
On Locality of Local Explanation Models Sahra Ghalebikesabi Lucile Ter-Minassian Karla Diaz-Ordaz Chris Holmes FedML FAtt 26 39 0 24 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 37 65 0 23 Jun 2021
A Framework for Evaluating Post Hoc Feature-Additive Explainers Zachariah Carmichael Walter J. Scheirer FAtt 46 4 0 15 Jun 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
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang 19 41 0 04 Jun 2021
On Efficiently Explaining Graph-Based Classifiers Xuanxiang Huang Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 34 37 0 02 Jun 2021
Probabilistic Sufficient Explanations Eric Wang Pasha Khosravi Mathias Niepert XAI FAtt TPM 30 23 0 21 May 2021
Evaluating the Correctness of Explainable AI Algorithms for Classification Orcun Yalcin Xiuyi Fan Siyuan Liu XAI FAtt 16 15 0 20 May 2021
Information-theoretic Evolution of Model Agnostic Global Explanations Sukriti Verma Nikaash Puri Piyush B. Gupta Balaji Krishnamurthy FAtt 29 0 0 14 May 2021
SAT-Based Rigorous Explanations for Decision Lists Alexey Ignatiev Sasha Rubin XAI 25 44 0 14 May 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar A. Madry FAtt 22 88 0 11 May 2021
Towards Rigorous Interpretations: a Formalisation of Feature Attribution Darius Afchar Romain Hennequin Vincent Guigue FAtt 31 20 0 26 Apr 2021
Shapley Explanation Networks Rui Wang Xiaoqian Wang David I. Inouye TDI FAtt 24 44 0 06 Apr 2021
Local Interpretations for Explainable Natural Language Processing: A Survey Siwen Luo Hamish Ivison S. Han Josiah Poon MILM 33 48 0 20 Mar 2021
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery M. Vowels Necati Cihan Camgöz Richard Bowden CML 37 296 0 03 Mar 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 33 22 0 14 Feb 2021
What does LIME really see in images? Damien Garreau Dina Mardaoui FAtt 19 38 0 11 Feb 2021
GeCo: Quality Counterfactual Explanations in Real Time Maximilian Schleich Zixuan Geng Yihong Zhang D. Suciu 46 61 0 05 Jan 2021
Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning Jiaheng Xie Xinyu Liu HAI 31 10 0 21 Dec 2020
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 21 63 0 18 Dec 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 36 241 0 21 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 27 99 0 10 Nov 2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks Wanqian Yang Lars Lorch Moritz Graule Himabindu Lakkaraju Finale Doshi-Velez UQCV BDL 17 16 0 21 Oct 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 30 51 0 03 Sep 2020
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data Christopher D. Blakely Ole-Christoffer Granmo 30 16 0 27 Jul 2020
Improving LIME Robustness with Smarter Locality Sampling Sean Saito Eugene Chua Nicholas Capel Rocco Hu FAtt AAML 9 22 0 22 Jun 2020
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 28 32 0 16 Jun 2020
How Interpretable and Trustworthy are GAMs? C. Chang S. Tan Benjamin J. Lengerich Anna Goldenberg R. Caruana FAtt 14 77 0 11 Jun 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 26 8 0 23 Apr 2020
Model Agnostic Multilevel Explanations K. Ramamurthy B. Vinzamuri Yunfeng Zhang Amit Dhurandhar 23 41 0 12 Mar 2020
NestedVAE: Isolating Common Factors via Weak Supervision M. Vowels Necati Cihan Camgöz Richard Bowden CML DRL 26 21 0 26 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
Deceptive AI Explanations: Creation and Detection Johannes Schneider Christian Meske Michalis Vlachos 14 28 0 21 Jan 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 13 132 0 20 Dec 2019
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance Giles Hooker L. Mentch Siyu Zhou 37 153 0 01 May 2019