"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
Explainability and Adversarial Robustness for RNNs Alexander Hartl Maximilian Bachl J. Fabini Tanja Zseby AAML 22 32 0 20 Dec 2019
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 13 132 0 20 Dec 2019
A Framework for Explainable Text Classification in Legal Document Review Christian J. Mahoney Jianping Zhang Nathaniel Huber-Fliflet Peter Gronvall Haozhen Zhao AILaw 19 32 0 19 Dec 2019
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting Bryan Lim Sercan Ö. Arik Nicolas Loeff Tomas Pfister AI4TS 66 1,415 0 19 Dec 2019
Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations Andreas Holzinger André M. Carrington Heimo Muller LRM XAI ELM 22 300 0 19 Dec 2019
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics Debjani Saha Candice Schumann Duncan C. McElfresh John P. Dickerson Michelle L. Mazurek Michael Carl Tschantz FaML 32 16 0 17 Dec 2019
Fine-grained Classification of Rowing teams M.J.A. van Wezel L. J. Hamburger Y. Napolean 32 1 0 11 Dec 2019
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches Kacper Sokol Peter A. Flach XAI 19 299 0 11 Dec 2019
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization Zhuo Wang Wei Zhang Ning Liu Jianyong Wang 19 29 0 10 Dec 2019
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Akanksha Atrey Kaleigh Clary David D. Jensen FAtt LRM 21 90 0 09 Dec 2019
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers Divyat Mahajan Chenhao Tan Amit Sharma OOD CML 28 206 0 06 Dec 2019
Neural Machine Translation: A Review and Survey Felix Stahlberg 3DV AI4TS MedIm 30 313 0 04 Dec 2019
Counterfactual Explanation Algorithms for Behavioral and Textual Data Yanou Ramon David Martens F. Provost Theodoros Evgeniou FAtt 31 87 0 04 Dec 2019
Explainable artificial intelligence model to predict acute critical illness from electronic health records S. Lauritsen Mads Kristensen Mathias Vassard Olsen Morten Skaarup Larsen K. M. Lauritsen Marianne Johansson Jørgensen Jeppe Lange B. Thiesson 21 298 0 03 Dec 2019
Automated Dependence Plots David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar 12 1 0 02 Dec 2019
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications Xiao Zhang Manish Marwah I-Ta Lee M. Arlitt Dan Goldwasser 29 14 0 01 Dec 2019
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods Sheikh Rabiul Islam W. Eberle S. Ghafoor XAI 22 42 0 22 Nov 2019
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response Sheikh Rabiul Islam W. Eberle S. Ghafoor Ambareen Siraj Mike Rogers 16 39 0 22 Nov 2019
Natural Language Generation Challenges for Explainable AI Ehud Reiter 19 39 0 20 Nov 2019
LionForests: Local Interpretation of Random Forests Ioannis Mollas Nick Bassiliades I. Vlahavas Grigorios Tsoumakas 19 12 0 20 Nov 2019
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization Shiori Sagawa Pang Wei Koh Tatsunori B. Hashimoto Percy Liang OOD 16 1,200 0 20 Nov 2019
PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems Azin Ghazimatin Oana Balalau Rishiraj Saha Roy Gerhard Weikum FAtt 27 97 0 19 Nov 2019
An explanation method for Siamese neural networks Lev V. Utkin M. Kovalev E. Kasimov 27 14 0 18 Nov 2019
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations Xijie Huang M. Alzantot Mani B. Srivastava AAML 17 103 0 18 Nov 2019
Causality-based Feature Selection: Methods and Evaluations Kui Yu Xianjie Guo Lin Liu Jiuyong Li Hao Wang Zhaolong Ling Xindong Wu CML 24 92 0 17 Nov 2019
On the computation of counterfactual explanations -- A survey André Artelt Barbara Hammer LRM 30 50 0 15 Nov 2019
Question-Conditioned Counterfactual Image Generation for VQA Jingjing Pan Yash Goyal Stefan Lee EgoV OOD 22 19 0 14 Nov 2019
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine Weisi Guo 32 40 0 11 Nov 2019
Social Bias Frames: Reasoning about Social and Power Implications of Language Maarten Sap Saadia Gabriel Lianhui Qin Dan Jurafsky Noah A. Smith Yejin Choi 42 486 0 10 Nov 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 35 805 0 06 Nov 2019
Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding Yi-Chieh Liu Yung-An Hsieh Min-Hung Chen Chao-Han Huck Yang Jesper N. Tegnér Y. Tsai 45 19 0 06 Nov 2019
Weight of Evidence as a Basis for Human-Oriented Explanations David Alvarez-Melis Hal Daumé Jennifer Wortman Vaughan Hanna M. Wallach XAI FAtt 24 20 0 29 Oct 2019
Feature relevance quantification in explainable AI: A causal problem Dominik Janzing Lenon Minorics Patrick Blobaum FAtt CML 24 279 0 29 Oct 2019
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control Mo Yu Shiyu Chang Yang Zhang Tommi Jaakkola 21 140 0 29 Oct 2019
bLIMEy: Surrogate Prediction Explanations Beyond LIME Kacper Sokol Alexander Hepburn Raúl Santos-Rodríguez Peter A. Flach FAtt 19 38 0 29 Oct 2019
A Game Theoretic Approach to Class-wise Selective Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 22 60 0 28 Oct 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 40 206 0 27 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 41 6,125 0 22 Oct 2019
Digital Twin approach to Clinical DSS with Explainable AI Dattaraj J. Rao Shraddha Mane 11 14 0 22 Oct 2019
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework Homayun Afrabandpey Tomi Peltola Juho Piironen Aki Vehtari Samuel Kaski 25 3 0 21 Oct 2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 25 411 0 18 Oct 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
Effect of Superpixel Aggregation on Explanations in LIME -- A Case Study with Biological Data Ludwig Schallner Johannes Rabold O. Scholz Ute Schmid FAtt 46 22 0 17 Oct 2019
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms Z. Q. Lin M. Shafiee S. Bochkarev Michael St. Jules Xiao Yu Wang A. Wong FAtt 29 80 0 16 Oct 2019
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye C. Rowat Ilya Feige 18 180 0 14 Oct 2019
Eavesdrop the Composition Proportion of Training Labels in Federated Learning Lixu Wang Shichao Xu Tianlin Li Qi Zhu FedML 25 63 0 14 Oct 2019
Measuring Unfairness through Game-Theoretic Interpretability Juliana Cesaro Fabio Gagliardi Cozman FAtt 16 13 0 12 Oct 2019
Testing and verification of neural-network-based safety-critical control software: A systematic literature review Jin Zhang Jingyue Li 25 47 0 05 Oct 2019
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt AAML 34 60 0 04 Oct 2019
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion A. Hinterreiter Peter Ruch Holger Stitz Martin Ennemoser J. Bernard Hendrik Strobelt M. Streit 27 43 0 02 Oct 2019