"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"

50 / 4,309 papers shown

Title
Learning Propagation Rules for Attribution Map Generation Yiding Yang Jiayan Qiu Xiuming Zhang Dacheng Tao Xinchao Wang FAtt 38 17 0 14 Oct 2020
Neural Databases James Thorne Majid Yazdani Marzieh Saeidi Fabrizio Silvestri Sebastian Riedel A. Halevy NAI 34 9 0 14 Oct 2020
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks Minh Nhat Vu My T. Thai BDL 18 328 0 12 Oct 2020
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? Jasmijn Bastings Katja Filippova XAI LRM 64 175 0 12 Oct 2020
Diptychs of human and machine perceptions Vivien A. Cabannes Thomas Kerdreux L. Thiry 28 0 0 12 Oct 2020
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations Andrea Ferrario M. Loi 25 5 0 09 Oct 2020
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision Max Glockner Ivan Habernal Iryna Gurevych LRM 32 25 0 07 Oct 2020
Global Optimization of Objective Functions Represented by ReLU Networks Christopher A. Strong Haoze Wu Aleksandar Zeljić Kyle D. Julian Guy Katz Clark W. Barrett Mykel J. Kochenderfer AAML 17 33 0 07 Oct 2020
PRover: Proof Generation for Interpretable Reasoning over Rules Swarnadeep Saha Sayan Ghosh Shashank Srivastava Joey Tianyi Zhou ReLM LRM 36 77 0 06 Oct 2020
Astraea: Grammar-based Fairness Testing E. Soremekun Sakshi Udeshi Sudipta Chattopadhyay 26 27 0 06 Oct 2020
Visualizing Color-wise Saliency of Black-Box Image Classification Models Yuhki Hatakeyama Hiroki Sakuma Yoshinori Konishi Kohei Suenaga FAtt 32 3 0 06 Oct 2020
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting Sayna Ebrahimi Suzanne Petryk Akash Gokul William Gan Joseph E. Gonzalez Marcus Rohrbach Trevor Darrell CLL 37 45 0 04 Oct 2020
Explaining Deep Neural Networks Oana-Maria Camburu XAI FAtt 38 26 0 04 Oct 2020
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers Hanjie Chen Yangfeng Ji AAML VLM 20 63 0 01 Oct 2020
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking M. Schlichtkrull Nicola De Cao Ivan Titov AI4CE 36 214 0 01 Oct 2020
Assessing Robustness of Text Classification through Maximal Safe Radius Computation Emanuele La Malfa Min Wu Luca Laurenti Benjie Wang Anthony Hartshorn Marta Z. Kwiatkowska AAML 20 18 0 01 Oct 2020
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task Han-Ching Wu Wenjie Ruan Jiangtao Wang Dingchang Zheng Bei Liu ... Xiangfei Chai Jian Chen Kunwei Li Shaolin Li A. Helal 32 25 0 30 Sep 2020
Accurate and Robust Feature Importance Estimation under Distribution Shifts Jayaraman J. Thiagarajan V. Narayanaswamy Rushil Anirudh P. Bremer A. Spanias OOD 21 9 0 30 Sep 2020
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach Nicholas F Halliwell Freddy Lecue FAtt 25 9 0 29 Sep 2020
Where is the Model Looking At?--Concentrate and Explain the Network Attention Wenjia Xu Jiuniu Wang Yang Wang Guangluan Xu Wei Dai Yirong Wu XAI 32 17 0 29 Sep 2020
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing Ping Lang Xiongjun Fu M. Martorella Jian Dong Rui Qin Xianpeng Meng M. Xie 26 39 0 29 Sep 2020
Instance-based Counterfactual Explanations for Time Series Classification Eoin Delaney Derek Greene Mark T. Keane CML AI4TS 21 89 0 28 Sep 2020
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach Rémi Eyraud Stéphane Ayache 26 16 0 28 Sep 2020
VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection Liang Gou Lincan Zou Nanxiang Li M. Hofmann A. Shekar A. Wendt Liu Ren 36 60 0 27 Sep 2020
An Explainable Model for EEG Seizure Detection based on Connectivity Features Mohamad Mansour Fouad Khnaisser Hmayag Partamian 14 6 0 26 Sep 2020
Landscape of R packages for eXplainable Artificial Intelligence Szymon Maksymiuk Alicja Gosiewska P. Biecek XAI 43 21 0 24 Sep 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 34 93 0 22 Sep 2020
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images Lucas O. Teixeira R. M. Pereira Diego Bertolini Luiz Eduardo Soares de Oliveira L. Nanni George D. C. Cavalcanti Yandre M. G. Costa 23 114 0 21 Sep 2020
Machine Guides, Human Supervises: Interactive Learning with Global Explanations Teodora Popordanoska Mohit Kumar Stefano Teso 21 21 0 21 Sep 2020
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting Ozan Ozyegen Igor Ilic Mucahit Cevik FAtt AI4TS 24 2 0 18 Sep 2020
Contextual Semantic Interpretability Diego Marcos Ruth C. Fong Sylvain Lobry Rémi Flamary Nicolas Courty D. Tuia SSL 25 27 0 18 Sep 2020
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses Kaivalya Rawal Himabindu Lakkaraju 29 11 0 15 Sep 2020
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition Liangzhi Li Bowen Wang Manisha Verma Yuta Nakashima R. Kawasaki Hajime Nagahara OCL 23 49 0 14 Sep 2020
Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness Blake VanBerlo Matthew A. S. Ross Jonathan Rivard Ryan Booker 6 26 0 12 Sep 2020
A Game-Based Approach for Helping Designers Learn Machine Learning Concepts Chelsea M. Myers Jiachi Xie Jichen Zhu 24 4 0 11 Sep 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 46 62 0 11 Sep 2020
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier Courtney Ford Eoin M. Kenny Mark T. Keane 23 6 0 10 Sep 2020
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning Eoin M. Kenny Mark T. Keane 28 99 0 10 Sep 2020
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset Erico Tjoa Cuntai Guan XAI FAtt 21 27 0 07 Sep 2020
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring Nijat Mehdiyev Peter Fettke AI4TS 25 55 0 04 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 33 51 0 03 Sep 2020
Explainable Empirical Risk Minimization Linli Zhang Georgios Karakasidis Arina Odnoblyudova Leyla Dogruel Alex Jung 27 5 0 03 Sep 2020
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images Xinyi Huang Suphanut Jamonnak Ye Zhao Boyu Wang Minh Hoai Kevin Yager Wei Xu 30 5 0 03 Sep 2020
Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy Donald R. Honeycutt Mahsan Nourani Eric D. Ragan HAI 38 61 0 28 Aug 2020
SHAP values for Explaining CNN-based Text Classification Models Wei Zhao Tarun Joshi V. Nair Agus Sudjianto FAtt 28 36 0 26 Aug 2020
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems Mahsan Nourani J. King Eric D. Ragan 25 99 0 20 Aug 2020
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP Sven Weinzierl Sandra Zilker Jens Brunk K. Revoredo Martin Matzner J. Becker 28 26 0 18 Aug 2020
Survey of XAI in digital pathology Milda Pocevičiūtė Gabriel Eilertsen Claes Lundström 14 56 0 14 Aug 2020
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis Ming Fan Wenying Wei Xiaofei Xie Yang Liu X. Guan Ting Liu FAtt AAML 27 36 0 13 Aug 2020
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Sasha Rubin Thomas Gerspacher Martin C. Cooper Alexey Ignatiev Nina Narodytska FAtt 30 59 0 13 Aug 2020