"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 / 4,337 papers shown

Title
Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT Anmol Nayak Hariprasad Timmapathini 35 4 0 01 Jun 2021
Efficient Explanations With Relevant Sets Yacine Izza Alexey Ignatiev Nina Narodytska Martin C. Cooper Sasha Rubin FAtt 25 15 0 01 Jun 2021
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 33 68 0 01 Jun 2021
Understanding peacefulness through the world news Vasiliki Voukelatou Ioanna Miliou F. Giannotti Luca Pappalardo 6 0 0 01 Jun 2021
Explanations for Monotonic Classifiers Sasha Rubin Thomas Gerspacher M. Cooper Alexey Ignatiev Nina Narodytska FAtt 16 43 0 01 Jun 2021
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization Jiaao Chen Dinghan Shen Weizhu Chen Diyi Yang BDL 27 47 0 31 May 2021
Bounded logit attention: Learning to explain image classifiers Thomas Baumhauer D. Slijepcevic Matthias Zeppelzauer FAtt 27 2 0 31 May 2021
An exact counterfactual-example-based approach to tree-ensemble models interpretability P. Blanchart 16 4 0 31 May 2021
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness Ruiwen Li Zhibo Zhang Jiani Li C. Trabelsi Scott Sanner Jongseong Jang Yeonjeong Jeong Dongsub Shim AAML 21 1 0 29 May 2021
Do not explain without context: addressing the blind spot of model explanations Katarzyna Wo'znica Katarzyna Pkekala Hubert Baniecki Wojciech Kretowicz El.zbieta Sienkiewicz P. Biecek 28 1 0 28 May 2021
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation Vijit Malik Rishabh Sanjay S. Nigam Kripabandhu Ghosh S. Guha Arnab Bhattacharya Ashutosh Modi ELM AILaw 29 144 0 28 May 2021
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data Yuanzheng Hu Marina Sokolova 19 7 0 27 May 2021
Sinan: Data-Driven, QoS-Aware Cluster Management for Microservices Yanqi Zhang Weizhe Hua Zhuangzhuang Zhou Ed Suh Christina Delimitrou 17 0 0 27 May 2021
CrystalCandle: A User-Facing Model Explainer for Narrative Explanations Jilei Yang Diana M. Negoescu P. Ahammad 16 1 0 27 May 2021
Fooling Partial Dependence via Data Poisoning Hubert Baniecki Wojciech Kretowicz P. Biecek AAML 34 23 0 26 May 2021
Towards Transparent Application of Machine Learning in Video Processing L. Murn Marc Górriz Blanch M. Santamaría F. Rivera M. Mrak 23 1 0 26 May 2021
An Explainable Probabilistic Classifier for Categorical Data Inspired to Quantum Physics E. Guidotti Alfio Ferrara 22 3 0 26 May 2021
Principal Component Hierarchy for Sparse Quadratic Programs R. Vreugdenhil Viet Anh Nguyen Armin Eftekhari Peyman Mohajerin Esfahani 36 2 0 25 May 2021
Deep Descriptive Clustering Hongjing Zhang Ian Davidson 22 6 0 24 May 2021
Word-level Text Highlighting of Medical Texts for Telehealth Services Ozan Ozyegen D. Kabe Mucahit Cevik 68 17 0 21 May 2021
On Explaining Random Forests with SAT Yacine Izza Sasha Rubin FAtt 30 72 0 21 May 2021
Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework K. Morik Helena Kotthaus Raphael Fischer Sascha Mucke Matthias Jakobs Nico Piatkowski Andrea Pauly Lukas Heppe Danny Heinrich 19 11 0 21 May 2021
Explainable Machine Learning with Prior Knowledge: An Overview Katharina Beckh Sebastian Müller Matthias Jakobs Vanessa Toborek Hanxiao Tan Raphael Fischer Pascal Welke Sebastian Houben Laura von Rueden XAI 27 28 0 21 May 2021
Probabilistic Sufficient Explanations Eric Wang Pasha Khosravi Guy Van den Broeck XAI FAtt TPM 30 23 0 21 May 2021
Explainable Activity Recognition for Smart Home Systems Devleena Das Yasutaka Nishimura R. Vivek Naoto Takeda Sean T. Fish Thomas Ploetz Sonia Chernova 26 41 0 20 May 2021
Evaluating the Correctness of Explainable AI Algorithms for Classification Orcun Yalcin Xiuyi Fan Siyuan Liu XAI FAtt 21 15 0 20 May 2021
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks Thorben Funke Megha Khosla Mandeep Rathee Avishek Anand FAtt 28 38 0 18 May 2021
Self-interpretable Convolutional Neural Networks for Text Classification Wei Zhao Rahul Singh Tarun Joshi Agus Sudjianto V. Nair FAtt MILM 23 6 0 18 May 2021
Algorithm-Agnostic Explainability for Unsupervised Clustering Charles A. Ellis M. Sendi Eloy P. T. Geenjaar Sergey Plis Robyn L. Miller Vince D. Calhoun 21 25 0 17 May 2021
Fine-grained Interpretation and Causation Analysis in Deep NLP Models Hassan Sajjad Narine Kokhlikyan Fahim Dalvi Nadir Durrani MILM 35 8 0 17 May 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 34 140 0 17 May 2021
How to Explain Neural Networks: an Approximation Perspective Hangcheng Dong Bingguo Liu Fengdong Chen Dong Ye Guodong Liu FAtt 31 1 0 17 May 2021
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence Scott Cheng-Hsin Yang Tomas Folke Patrick Shafto 21 5 0 16 May 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 34 184 0 15 May 2021
Cohort Shapley value for algorithmic fairness Masayoshi Mase Art B. Owen Benjamin B. Seiler 26 14 0 15 May 2021
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models Omid Davoodi Majid Komeili FAtt OffRL 27 6 0 14 May 2021
Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks Mohammad Nokhbeh Zaeem Majid Komeili CML 25 9 0 14 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
DoS and DDoS Mitigation Using Variational Autoencoders Eirik Molde Bårli Anis Yazidi E. Herrera-Viedma H. Haugerud AAML DRL 8 16 0 14 May 2021
SAT-Based Rigorous Explanations for Decision Lists Alexey Ignatiev Sasha Rubin XAI 32 44 0 14 May 2021
Discovering the Rationale of Decisions: Experiments on Aligning Learning and Reasoning Cor Steging S. Renooij Bart Verheij 12 21 0 14 May 2021
XAI Handbook: Towards a Unified Framework for Explainable AI Sebastián M. Palacio Adriano Lucieri Mohsin Munir Jörn Hees Sheraz Ahmed Andreas Dengel 25 32 0 14 May 2021
Bias, Fairness, and Accountability with AI and ML Algorithms Neng-Zhi Zhou Zach Zhang V. Nair Harsh Singhal Jie Chen Agus Sudjianto FaML 21 9 0 13 May 2021
Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss E. Ferrari D. Bacciu FaML AI4CE 33 6 0 13 May 2021
Explainable Machine Learning for Fraud Detection I. Psychoula A. Gutmann Pradip Mainali Sharon H. Lee Paul Dunphy F. Petitcolas FaML 77 36 0 13 May 2021
Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey Xiaoyu Zhang Chao Chen Yi Xie Xiaofeng Chen Jun Zhang Yang Xiang FedML 27 7 0 13 May 2021
Sufficient reasons for classifier decisions in the presence of constraints Niku Gorji S. Rubin 17 3 0 12 May 2021
What's wrong with this video? Comparing Explainers for Deepfake Detection Samuele Pino Mark J. Carman Paolo Bestagini AAML 20 7 0 12 May 2021
A Graph Neural Network Approach for Product Relationship Prediction Faez Ahmed Yaxin Cui Yan Fu Wei Chen GNN 30 7 0 12 May 2021
Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI Wei Xu Marvin Dainoff Liezhong Ge Zaifeng Gao 59 169 0 12 May 2021