"Why Should I Trust You?": Explaining the Predictions of Any Classifier

16 February 2016

Papers citing ""Why Should I Trust You?": Explaining the Predictions of Any Classifier"

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Title
Best of both worlds: local and global explanations with human-understandable concepts Jessica Schrouff Sebastien Baur Shaobo Hou Diana Mincu Eric Loreaux Ralph Blanes James Wexler Alan Karthikesalingam Been Kim FAtt 34 28 0 16 Jun 2021
Counterfactual Graphs for Explainable Classification of Brain Networks Carlo Abrate Francesco Bonchi CML 30 55 0 16 Jun 2021
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning M. Velmurugan Chun Ouyang Catarina Moreira Renuka Sindhgatta XAI 29 16 0 16 Jun 2021
A Framework for Evaluating Post Hoc Feature-Additive Explainers Zachariah Carmichael Walter J. Scheirer FAtt 51 4 0 15 Jun 2021
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach Johannes Rabold M. Siebers Ute Schmid 31 14 0 15 Jun 2021
S-LIME: Stabilized-LIME for Model Explanation Zhengze Zhou Giles Hooker Fei Wang FAtt 30 88 0 15 Jun 2021
Keep CALM and Improve Visual Feature Attribution Jae Myung Kim Junsuk Choe Zeynep Akata Seong Joon Oh FAtt 350 20 0 15 Jun 2021
Controlling Neural Networks with Rule Representations Sungyong Seo Sercan O. Arik Jinsung Yoon Xiang Zhang Kihyuk Sohn Tomas Pfister OOD AI4CE 37 35 0 14 Jun 2021
Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities Shreyan Chowdhury Verena Praher Gerhard Widmer 13 14 0 14 Jun 2021
Pitfalls of Explainable ML: An Industry Perspective Sahil Verma Aditya Lahiri John P. Dickerson Su-In Lee XAI 21 9 0 14 Jun 2021
Counterfactual Explanations as Interventions in Latent Space Riccardo Crupi Alessandro Castelnovo D. Regoli Beatriz San Miguel González CML 16 24 0 14 Jun 2021
Characterizing the risk of fairwashing Ulrich Aïvodji Hiromi Arai Sébastien Gambs Satoshi Hara 23 27 0 14 Jun 2021
Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI Kiana Alikhademi Brianna Richardson E. Drobina J. Gilbert 38 33 0 14 Jun 2021
Certification of embedded systems based on Machine Learning: A survey Guillaume Vidot Christophe Gabreau I. Ober Iulian Ober 16 12 0 14 Jun 2021
FairCanary: Rapid Continuous Explainable Fairness Avijit Ghosh Aalok Shanbhag Christo Wilson 19 20 0 13 Jun 2021
Entropy-based Logic Explanations of Neural Networks Pietro Barbiero Gabriele Ciravegna Francesco Giannini Pietro Lio Marco Gori S. Melacci FAtt XAI 30 78 0 12 Jun 2021
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features F. Ventura Salvatore Greco D. Apiletti Tania Cerquitelli 14 1 0 12 Jun 2021
Local Explanation of Dialogue Response Generation Yi-Lin Tuan Connor Pryor Wenhu Chen Lise Getoor Wenjie Wang 30 11 0 11 Jun 2021
FedNLP: An interpretable NLP System to Decode Federal Reserve Communications Jean Lee Hoyoul Luis Youn Nicholas Stevens Josiah Poon S. Han 24 10 0 11 Jun 2021
Interpreting Expert Annotation Differences in Animal Behavior Megan Tjandrasuwita Jennifer J. Sun Ann Kennedy Swarat Chaudhuri Yisong Yue 19 8 0 11 Jun 2021
Cross-lingual Emotion Detection Sabit Hassan Shaden Shaar Kareem Darwish 32 12 0 10 Jun 2021
On the overlooked issue of defining explanation objectives for local-surrogate explainers Rafael Poyiadzi X. Renard Thibault Laugel Raúl Santos-Rodríguez Marcin Detyniecki 21 6 0 10 Jun 2021
Explainable AI, but explainable to whom? Julie Gerlings Millie Søndergaard Jensen Arisa Shollo 46 43 0 10 Jun 2021
An Interpretable Neural Network for Parameter Inference Johann Pfitzinger 34 0 0 10 Jun 2021
Explaining Time Series Predictions with Dynamic Masks Jonathan Crabbé M. Schaar FAtt AI4TS 30 80 0 09 Jun 2021
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems Andrea Apicella Salvatore Giugliano Francesco Isgrò R. Prevete 14 18 0 09 Jun 2021
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization Léo Andéol Yusei Kawakami Yuichiro Wada Takafumi Kanamori K. Müller G. Montavon OOD 44 7 0 09 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer Sina Mohseni Haotao Wang Zhiding Yu Chaowei Xiao Zhangyang Wang J. Yadawa 31 32 0 09 Jun 2021
On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation Wei Zhang Ziming Huang Yada Zhu Guangnan Ye Xiaodong Cui Fan Zhang 60 17 0 09 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 16 21 0 08 Jun 2021
White Paper Assistance: A Step Forward Beyond the Shortcut Learning Xuan Cheng Tianshu Xie Xiaomin Wang Jiali Deng Minghui Liu Meilin Liu AAML 26 0 0 08 Jun 2021
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models Sahil Verma Keegan E. Hines John P. Dickerson 22 23 0 07 Jun 2021
Accurate Shapley Values for explaining tree-based models Salim I. Amoukou Nicolas Brunel Tangi Salaun TDI FAtt 16 13 0 07 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 Aleksander Madry 40 40 0 07 Jun 2021
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems Jeff Druce M. Harradon J. Tittle XAI 11 16 0 07 Jun 2021
Causal Abstractions of Neural Networks Atticus Geiger Hanson Lu Thomas Icard Christopher Potts NAI CML 33 228 0 06 Jun 2021
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning Yatao Bian Yu Rong Tingyang Xu Jiaxiang Wu Andreas Krause Junzhou Huang 51 16 0 05 Jun 2021
Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions Akihisa Watanabe Michiya Kuramata Kaito Majima Haruka Kiyohara Kensho Kondo Kazuhide Nakata AI4CE 17 2 0 05 Jun 2021
Impact of data-splits on generalization: Identifying COVID-19 from cough and context Makkunda Sharma Nikhil Shenoy Jigar Doshi Piyush Bagad Aman Dalmia Parag Bhamare A. Mahale S. Rane Neeraj Agrawal R. Panicker OOD 61 4 0 05 Jun 2021
Counterfactual Explanations Can Be Manipulated Dylan Slack Sophie Hilgard Himabindu Lakkaraju Sameer Singh 20 136 0 04 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 47 41 0 04 Jun 2021
Evaluating Local Explanations using White-box Models Amir Hossein Akhavan Rahnama Judith Butepage Pierre Geurts Henrik Bostrom FAtt 30 0 0 04 Jun 2021
Finding and Fixing Spurious Patterns with Explanations Gregory Plumb Marco Tulio Ribeiro Ameet Talwalkar 43 41 0 03 Jun 2021
Exploring Distantly-Labeled Rationales in Neural Network Models Quzhe Huang Shengqi Zhu Yansong Feng Dongyan Zhao 12 10 0 03 Jun 2021
Causality in Neural Networks -- An Extended Abstract Abbavaram Gowtham Reddy CML OOD 16 1 0 03 Jun 2021
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution Jiacheng Xu Greg Durrett 38 16 0 03 Jun 2021
Towards an Explanation Space to Align Humans and Explainable-AI Teamwork G. Cabour A. Morales É. Ledoux S. Bassetto 30 5 0 02 Jun 2021
On Efficiently Explaining Graph-Based Classifiers Xuanxiang Huang Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 55 37 0 02 Jun 2021
When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis Zhe Liu Yufan Guo J. Mahmud 17 9 0 02 Jun 2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations Peter Hase Harry Xie Joey Tianyi Zhou OODD LRM FAtt 43 91 0 01 Jun 2021