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Explanation in Artificial Intelligence: Insights from the Social Sciences

22 June 2017

Papers citing "Explanation in Artificial Intelligence: Insights from the Social Sciences"

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Title
A Bayesian Account of Measures of Interpretability in Human-AI Interaction S. Sreedharan Anagha Kulkarni Tathagata Chakraborti David E. Smith S. Kambhampati 167 10 0 22 Nov 2020
Explaining by Removing: A Unified Framework for Model ExplanationJournal of machine learning research (JMLR), 2020 Ian Covert Scott M. Lundberg Su-In Lee FAtt 349 299 0 21 Nov 2020
Iterative Planning with Plan-Space Explanations: A Tool and User Study Rebecca Eifler Jörg Hoffmann LRM 54 2 0 19 Nov 2020
RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing Contrastive Explanations and Revised Plan SuggestionsInternational Conference on Automated Planning and Scheduling (ICAPS), 2020 Kaya Stechly S. Sreedharan Sailik Sengupta Subbarao Kambhampati 156 9 0 19 Nov 2020
A Survey on the Explainability of Supervised Machine LearningJournal of Artificial Intelligence Research (JAIR), 2020 Nadia Burkart Marco F. Huber FaML XAI 224 878 0 16 Nov 2020
Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science Adam J. Johs Denise E. Agosto Rosina O. Weber 172 7 0 13 Nov 2020
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 413 119 0 10 Nov 2020
Interpretable collaborative data analysis on distributed data A. Imakura Hiroaki Inaba Yukihiko Okada Tetsuya Sakurai FedML 87 27 0 09 Nov 2020
Feature Removal Is a Unifying Principle for Model Explanation Methods Ian Covert Scott M. Lundberg Su-In Lee FAtt 330 40 0 06 Nov 2020
Digital Nudging with Recommender Systems: Survey and Future Directions Mathias Jesse Dietmar Jannach 185 158 0 06 Nov 2020
Necessary and Sufficient Explanations in Abstract Argumentation A. Borg Floris Bex 45 2 0 04 Nov 2020
Towards Personalized Explanation of Robot Path Planning via User Feedback Kayla Boggess Shenghui Chen Lu Feng 112 4 0 01 Nov 2020
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test Zicun Cong Lingyang Chu Yu Yang Jian Pei 136 0 0 01 Nov 2020
ExplanationLP: Abductive Reasoning for Explainable Science Question Answering Mokanarangan Thayaparan Marco Valentino André Freitas LRM 175 9 0 25 Oct 2020
Measuring Association Between Labels and Free-Text RationalesConference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Sarah Wiegreffe Ana Marasović Noah A. Smith 636 194 0 24 Oct 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 209 7 0 23 Oct 2020
Model Interpretability through the Lens of Computational ComplexityNeural Information Processing Systems (NeurIPS), 2020 Pablo Barceló Mikaël Monet Jorge A. Pérez Bernardo Subercaseaux 323 109 0 23 Oct 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 202 99 0 21 Oct 2020
Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics Tiago Mota Mohan Sridharan 105 5 0 20 Oct 2020
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A ReviewACM Computing Surveys (ACM CSUR), 2020 Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 672 237 0 20 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio J. Herbinger AI4TS AI4CE 344 462 0 19 Oct 2020
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making Charvi Rastogi Yunfeng Zhang Dennis L. Wei Kush R. Varshney Amit Dhurandhar Richard J. Tomsett HAI 214 153 0 15 Oct 2020
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs Ana Marasović Chandra Bhagavatula J. S. Park Ronan Le Bras Noah A. Smith Yejin Choi ReLM LRM 204 63 0 15 Oct 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 621 536 0 15 Oct 2020
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? Jasmijn Bastings Katja Filippova XAI LRM 235 195 0 12 Oct 2020
Towards a Conversational Measure of Trust Mengyao Li Areen Alsaid Sofia I. Noejovich E. V. Cross John D. Lee HILM 62 4 0 10 Oct 2020
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations Andrea Ferrario M. Loi 148 5 0 09 Oct 2020
Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine LearningIEEE Transactions on Plasma Science (IEEE Trans. Plasma Sci.), 2020 Julia B. Nakhleh M. G. Fernández-Godino M. Grosskopf B. Wilson J. Kline G. Srinivasan 80 10 0 08 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 331 182 0 08 Oct 2020
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification Florentin Flambeau Jiechieu Kameni Norbert Tsopzé XAI FAtt MedIm 89 1 0 08 Oct 2020
Interpretable Sequence Classification via Discrete Optimization Maayan Shvo Andrew C. Li Rodrigo Toro Icarte Sheila A. McIlraith 91 26 0 06 Oct 2020
Efficient computation of contrastive explanationsIEEE International Joint Conference on Neural Network (IJCNN), 2020 André Artelt Barbara Hammer 177 9 0 06 Oct 2020
Explanation Ontology: A Model of Explanations for User-Centered AIInternational Workshop on the Semantic Web (SW), 2020 Shruthi Chari Oshani Seneviratne Daniel Gruen Morgan Foreman Amar K. Das D. McGuinness XAI 146 64 0 04 Oct 2020
A Survey on Explainability in Machine Reading Comprehension Mokanarangan Thayaparan Marco Valentino André Freitas FaML 302 52 0 01 Oct 2020
Explainable AI without Interpretable Model Kary Framling ELM 71 8 0 29 Sep 2020
Instance-based Counterfactual Explanations for Time Series Classification Eoin Delaney Derek Greene Mark T. Keane CML AI4TS 192 112 0 28 Sep 2020
Disentangled Neural Architecture SearchIEEE International Joint Conference on Neural Network (IJCNN), 2020 Xinyue Zheng Xuanjing Huang Qigang Wang Peng Wang AI4CE 143 4 0 24 Sep 2020
Local Post-Hoc Explanations for Predictive Process Monitoring in ManufacturingEuropean Conference on Information Systems (ECIS), 2020 Nijat Mehdiyev Peter Fettke 213 12 0 22 Sep 2020
ALICE: Active Learning with Contrastive Natural Language ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Weixin Liang James Zou Zhou Yu VLM 207 54 0 22 Sep 2020
Survey of explainable machine learning with visual and granular methods beyond quasi-explanationsStudies in Computational Intelligence (SCI), 2020 Boris Kovalerchuk M. Ahmad University of Washington Tacoma 209 50 0 21 Sep 2020
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects Falco J. Bargagli-Stoffi Riccardo Cadei Kwonsang Lee Francesca Dominici CML 487 25 0 18 Sep 2020
Principles and Practice of Explainable Machine LearningFrontiers in Big Data (Front. Big Data), 2020 Vaishak Belle I. Papantonis FaML 218 521 0 18 Sep 2020
Addressing Cognitive Biases in Augmented Business Decision Systems Thomas Baudel Manon Verbockhaven Guillaume Roy Victoire Cousergue Rida Laarach 83 3 0 17 Sep 2020
GLUCOSE: GeneraLized and COntextualized Story ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP), 2020 N. Mostafazadeh Aditya Kalyanpur Lori Moon David W. Buchanan Lauren Berkowitz Or Biran Jennifer Chu-Carroll 304 127 0 16 Sep 2020
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations F. Sperrle Mennatallah El-Assady G. Guo Duen Horng Chau Alex Endert Daniel A. Keim 151 23 0 14 Sep 2020
Is there a role for statistics in artificial intelligence?Advances in Data Analysis and Classification (ADAC), 2020 Sarah Friedrich G. Antes S. Behr Harald Binder W. Brannath ... H. Leitgöb Markus Pauly A. Steland A. Wilhelm T. Friede 152 59 0 13 Sep 2020
TREX: Tree-Ensemble Representer-Point Explanations Jonathan Brophy Daniel Lowd TDI 196 7 0 11 Sep 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial ExamplesMinds and Machines (MM), 2020 Timo Freiesleben GAN 452 69 0 11 Sep 2020
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep LearningAAAI Conference on Artificial Intelligence (AAAI), 2020 Eoin M. Kenny Mark T. Keane 192 113 0 10 Sep 2020
Beneficial and Harmful Explanatory Machine LearningMachine-mediated learning (ML), 2020 L. Ai Stephen Muggleton Céline Hocquette Mark Gromowski Ute Schmid 149 33 0 09 Sep 2020