"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
Testing Monotonicity of Machine Learning Models Arnab Sharma Heike Wehrheim 16 8 0 27 Feb 2020
The Emerging Landscape of Explainable AI Planning and Decision Making Tathagata Chakraborti S. Sreedharan S. Kambhampati 35 112 0 26 Feb 2020
NeuralSens: Sensitivity Analysis of Neural Networks J. Pizarroso J. Portela A. Muñoz 12 60 0 26 Feb 2020
xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems Vineel Nagisetty Laura Graves Joseph Scott Vijay Ganesh GAN DRL 26 27 0 24 Feb 2020
The Pragmatic Turn in Explainable Artificial Intelligence (XAI) Andrés Páez 27 191 0 22 Feb 2020
On The Reasons Behind Decisions Adnan Darwiche Auguste Hirth FaML 25 145 0 21 Feb 2020
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics Catarina Moreira Renuka Sindhgatta Chun Ouyang P. Bruza Andreas Wichert 28 4 0 21 Feb 2020
Learning Global Transparent Models Consistent with Local Contrastive Explanations Tejaswini Pedapati Avinash Balakrishnan Karthikeyan Shanmugam Amit Dhurandhar FAtt 22 0 0 19 Feb 2020
A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions Yiran Li Takanori Fujiwara Y. Choi Katherine K. Kim K. Ma OOD 24 27 0 18 Feb 2020
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation Sheng Shi Xinfeng Zhang Wei Fan FAtt 11 28 0 18 Feb 2020
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning Arash Kalatian Bilal Farooq 20 37 0 18 Feb 2020
On the Similarity of Deep Learning Representations Across Didactic and Adversarial Examples P. Douglas F. Farahani AAML 19 5 0 17 Feb 2020
Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks M. Leming Juan M Gorriz J. Suckling 19 51 0 14 Feb 2020
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis Jung Yeon Park K. T. Carr Stephan Zhang Yisong Yue Rose Yu 33 14 0 13 Feb 2020
Convex Density Constraints for Computing Plausible Counterfactual Explanations André Artelt Barbara Hammer 19 47 0 12 Feb 2020
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence S. Raschka Joshua Patterson Corey J. Nolet AI4CE 29 485 0 12 Feb 2020
Decisions, Counterfactual Explanations and Strategic Behavior Stratis Tsirtsis Manuel Gomez Rodriguez 27 58 0 11 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 32 143 0 10 Feb 2020
LUNAR: Cellular Automata for Drifting Data Streams J. Lobo Javier Del Ser Francisco Herrera AI4TS 19 4 0 06 Feb 2020
Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support Christian Meske Enrico Bunde 24 7 0 04 Feb 2020
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations S. Sreedharan Utkarsh Soni Mudit Verma Siddharth Srivastava S. Kambhampati 76 30 0 04 Feb 2020
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study Ahmed Alqaraawi M. Schuessler Philipp Weiß Enrico Costanza N. Bianchi-Berthouze AAML FAtt XAI 33 197 0 03 Feb 2020
Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation Rupam Patir Shubham Singhal C. Anantaram Vikram Goyal 16 0 0 02 Feb 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space Riccardo Guidotti A. Monreale Stan Matwin D. Pedreschi FAtt 24 67 0 27 Jan 2020
Visualisation of Medical Image Fusion and Translation for Accurate Diagnosis of High Grade Gliomas Nishant Kumar Nico Hoffmann M. Kirsch Stefan Gumhold MedIm 27 3 0 26 Jan 2020
How to Support Users in Understanding Intelligent Systems? Structuring the Discussion Malin Eiband Daniel Buschek H. Hussmann 45 28 0 22 Jan 2020
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems Zana Buçinca Phoebe Lin Krzysztof Z. Gajos Elena L. Glassman ELM 17 280 0 22 Jan 2020
Deceptive AI Explanations: Creation and Detection Johannes Schneider Christian Meske Michalis Vlachos 29 28 0 21 Jan 2020
Evaluating Weakly Supervised Object Localization Methods Right Junsuk Choe Seong Joon Oh Seungho Lee Sanghyuk Chun Zeynep Akata Hyunjung Shim WSOL 303 186 0 21 Jan 2020
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach Carlos Fernandez F. Provost Xintian Han CML 27 70 0 21 Jan 2020
An interpretable neural network model through piecewise linear approximation Mengzhuo Guo Qingpeng Zhang Xiuwu Liao D. Zeng MILM FAtt 21 7 0 20 Jan 2020
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition S. Raschka Benjamin Kaufman AI4CE 28 67 0 17 Jan 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh Dawei Yin Yi-Ju Chang FAtt 37 346 0 17 Jan 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 39 207 0 15 Jan 2020
CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis Yao Xie Melody Chen David Kao Ge Gao Xiang Ánthony' Chen 31 126 0 15 Jan 2020
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans Vivian Lai Han Liu Chenhao Tan 35 139 0 14 Jan 2020
On the Resilience of Biometric Authentication Systems against Random Inputs Benjamin Zi Hao Zhao Hassan Jameel Asghar M. Kâafar AAML 39 23 0 13 Jan 2020
Explaining the Explainer: A First Theoretical Analysis of LIME Damien Garreau U. V. Luxburg FAtt 11 172 0 10 Jan 2020
Theory In, Theory Out: The uses of social theory in machine learning for social science J. Radford K. Joseph 16 44 0 09 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 43 301 0 08 Jan 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 52 702 0 08 Jan 2020
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples Chelsea M. Myers Evan Freed Luis Fernando Laris Pardo Anushay Furqan S. Risi Jichen Zhu CML 18 12 0 07 Jan 2020
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making Yunfeng Zhang Q. V. Liao Rachel K. E. Bellamy 28 662 0 07 Jan 2020
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring Sibylle Hess W. Duivesteijn Decebal Constantin Mocanu 25 12 0 07 Jan 2020
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules Bishwamittra Ghosh Kuldeep S. Meel 15 34 0 07 Jan 2020
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability José Mena O. Pujol Jordi Vitrià 21 8 0 29 Dec 2019
On the Morality of Artificial Intelligence A. Luccioni Yoshua Bengio AI4TS FaML 27 24 0 26 Dec 2019
Smell Pittsburgh: Engaging Community Citizen Science for Air Quality Yen-Chia Hsu Jennifer L. Cross P. Dille Michael Tasota Beatrice Dias Randy Sargent Ting-Hao 'Kenneth' Huang I. Nourbakhsh 11 16 0 26 Dec 2019
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution Nikaash Puri Sukriti Verma Piyush B. Gupta Dhruv Kayastha Shripad Deshmukh Balaji Krishnamurthy Sameer Singh FAtt AAML 19 75 0 23 Dec 2019
Exploring Interpretability for Predictive Process Analytics Renuka Sindhgatta Chun Ouyang Catarina Moreira 10 2 0 22 Dec 2019