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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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Bandits for Learning to Explain from Explanations Freya Behrens Stefano Teso Davide Mottin FAtt 110 1 0 07 Feb 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural NetworksInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021 Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 513 172 0 05 Feb 2021
"I Don't Think So": Summarizing Policy Disagreements for Agent ComparisonAAAI Conference on Artificial Intelligence (AAAI), 2021 Yotam Amitai Ofra Amir LLMAG 125 13 0 05 Feb 2021
AI Development for the Public Interest: From Abstraction Traps to Sociotechnical RisksInternational Symposium on Technology and Society (ISTAS), 2020 Mckane Andrus Sarah Dean T. Gilbert Nathan Lambert Tom Zick 140 11 0 04 Feb 2021
EUCA: the End-User-Centered Explainable AI Framework Weina Jin Jianyu Fan D. Gromala Philippe Pasquier Ghassan Hamarneh 256 27 0 04 Feb 2021
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data Peter Hase Joey Tianyi Zhou XAI 385 91 0 03 Feb 2021
Directive Explanations for Actionable Explainability in Machine Learning Applications Ronal Singh Paul Dourish Piers Howe Tim Miller L. Sonenberg Eduardo Velloso F. Vetere 120 42 0 03 Feb 2021
Evaluating the Interpretability of Generative Models by Interactive ReconstructionInternational Conference on Human Factors in Computing Systems (CHI), 2021 A. Ross Nina Chen Elisa Zhao Hang Elena L. Glassman Finale Doshi-Velez 273 52 0 02 Feb 2021
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical LensInternational Conference on Human Factors in Computing Systems (CHI), 2021 Maia L. Jacobs Jeffrey He Melanie F. Pradier Barbara D. Lam Andrew C Ahn T. McCoy R. Perlis Finale Doshi-Velez Krzysztof Z. Gajos 188 159 0 01 Feb 2021
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep LearningArtificial Intelligence (AI), 2021 Matthew Lyle Olson Roli Khanna Lawrence Neal Fuxin Li Weng-Keen Wong CML 189 86 0 29 Jan 2021
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling David Harbecke AAML 164 2 0 28 Jan 2021
Cognitive Perspectives on Context-based Decisions and Explanations Marcus Westberg Kary Främling 39 2 0 25 Jan 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their NeedsInternational Conference on Human Factors in Computing Systems (CHI), 2021 Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 256 144 0 24 Jan 2021
Show or Suppress? Managing Input Uncertainty in Machine Learning Model ExplanationsArtificial Intelligence (AI), 2021 Danding Wang Wencan Zhang Brian Y. Lim FAtt 97 27 0 23 Jan 2021
Explainable Artificial Intelligence Approaches: A Survey Sheikh Rabiul Islam W. Eberle S. Ghafoor Mohiuddin Ahmed XAI 165 122 0 23 Jan 2021
A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual ExplanationsInternational Conference on Case-Based Reasoning (ICCBR), 2021 Barry Smyth Mark T. Keane CML 234 34 0 22 Jan 2021
GLocalX -- From Local to Global Explanations of Black Box AI ModelsArtificial Intelligence (AI), 2021 Mattia Setzu Riccardo Guidotti A. Monreale Franco Turini D. Pedreschi F. Giannotti 264 138 0 19 Jan 2021
Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making Han Liu Vivian Lai Chenhao Tan 364 135 0 13 Jan 2021
Expanding Explainability: Towards Social Transparency in AI systemsInternational Conference on Human Factors in Computing Systems (CHI), 2021 Upol Ehsan Q. V. Liao Michael J. Muller Mark O. Riedl Justin D. Weisz 252 474 0 12 Jan 2021
Machine Learning Uncertainty as a Design Material: A Post-Phenomenological InquiryInternational Conference on Human Factors in Computing Systems (CHI), 2021 J. Benjamin Arne Berger Nick Merrill James Pierce 198 102 0 11 Jan 2021
Argument Schemes and Dialogue for Explainable Planning Quratul-ain Mahesar Simon Parsons 67 2 0 07 Jan 2021
How Much Automation Does a Data Scientist Want? Dakuo Wang Q. V. Liao Yunfeng Zhang Udayan Khurana Horst Samulowitz Soya Park Michael J. Muller Lisa Amini AI4CE 207 57 0 07 Jan 2021
Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows C. Ryan Christophe Gúeret D. Berry Medb Corcoran Mark T. Keane Brian Mac Namee 147 9 0 06 Jan 2021
One-shot Policy Elicitation via Semantic Reward Manipulation Aaquib Tabrez Ryan Leonard Bradley Hayes 137 2 0 06 Jan 2021
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision MakingIEEE Transactions on Visualization and Computer Graphics (TVCG), 2021 Md. Naimul Hoque Klaus Mueller CML 314 36 0 03 Jan 2021
Modeling Disclosive Transparency in NLP Application DescriptionsConference on Empirical Methods in Natural Language Processing (EMNLP), 2021 Michael Stephen Saxon Sharon Levy Xinyi Wang Alon Albalak Wenjie Wang 306 7 0 02 Jan 2021
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving ModelsAnnual Meeting of the Association for Computational Linguistics (ACL), 2021 Tongshuang Wu Marco Tulio Ribeiro Jeffrey Heer Daniel S. Weld 319 280 0 01 Jan 2021
Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA Ana Valeria González Gagan Bansal Angela Fan Robin Jia Yashar Mehdad Srini Iyer AAML 183 24 0 30 Dec 2020
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in PythonJournal of machine learning research (JMLR), 2020 Hubert Baniecki Wojciech Kretowicz Piotr Piątyszek J. Wiśniewski P. Biecek FaML 242 123 0 28 Dec 2020
Explaining NLP Models via Minimal Contrastive Editing (MiCE)Findings (Findings), 2020 Alexis Ross Ana Marasović Matthew E. Peters 363 128 0 27 Dec 2020
Brain-inspired Search Engine Assistant based on Knowledge GraphIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020 Xuejiao Zhao Huanhuan Chen Zhenchang Xing Chunyan Miao 150 42 0 25 Dec 2020
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial LearningFrontiers in Artificial Intelligence (FAI), 2020 Silvan Mertes Tobias Huber Katharina Weitz Alexander Heimerl Elisabeth André GAN AAML MedIm 288 102 0 22 Dec 2020
Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep LearningJournal of Management Information Systems (JMIS), 2020 Jiaheng Xie Xinyu Liu HAI 402 14 0 21 Dec 2020
On Relating 'Why?' and 'Why Not?' Explanations Alexey Ignatiev Nina Narodytska Nicholas M. Asher Sasha Rubin XAI FAtt LRM 181 30 0 21 Dec 2020
Semantics and explanation: why counterfactual explanations produce adversarial examples in deep neural networks Kieran Browne Ben Swift AAML GAN 121 32 0 18 Dec 2020
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to Theory Nazanin Fouladgar Kary Främling XAI 59 5 0 17 Dec 2020
On Exploiting Hitting Sets for Model ReconciliationAAAI Conference on Artificial Intelligence (AAAI), 2020 Stylianos Loukas Vasileiou Alessandro Previti William Yeoh 153 28 0 16 Dec 2020
Explanation from Specification Harish Naik Gyorgy Turán XAI 115 0 0 13 Dec 2020
The Three Ghosts of Medical AI: Can the Black-Box Present Deliver? Thomas P. Quinn Stephan Jacobs M. Senadeera Vuong Le S. Coghlan 102 138 0 10 Dec 2020
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation LearningNeural Networks (NN), 2020 Haoteng Tang Guixiang Ma Lifang He Heng-Chiao Huang Chen Tang GNN 211 26 0 10 Dec 2020
Influence-Driven Explanations for Bayesian Network ClassifiersPacific Rim International Conference on Artificial Intelligence (PRICAI), 2020 Antonio Rago Emanuele Albini P. Baroni Francesca Toni 246 9 0 10 Dec 2020
Deep Argumentative Explanations Emanuele Albini Piyawat Lertvittayakumjorn Antonio Rago Francesca Toni AAML 244 5 0 10 Dec 2020
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learningInternational Conference on Human Factors in Computing Systems (CHI), 2020 Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 315 19 0 10 Dec 2020
Competition analysis on the over-the-counter credit default swap market L. Abraham 127 1 0 03 Dec 2020
Reviewing the Need for Explainable Artificial Intelligence (xAI)Hawaii International Conference on System Sciences (HICSS), 2020 Julie Gerlings Arisa Shollo Ioanna D. Constantiou 320 81 0 02 Dec 2020
Why Did the Robot Cross the Road? A User Study of Explanation in Human-Robot InteractionInteracción (HCI International), 2020 Zachary Taschdjian 60 0 0 30 Nov 2020
Investigating Human Response, Behaviour, and Preference in Joint-Task Interaction A. Lindsay B. Craenen S. Dalzel-Job Robin L. Hill Ronald P. A. Petrick 64 4 0 27 Nov 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their ExplanationsComputer Vision and Pattern Recognition (CVPR), 2020 Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 484 126 0 25 Nov 2020
Model Elicitation through Direct Questioning Sachin Grover David E. Smith S. Kambhampati 115 4 0 24 Nov 2020
The Interpretable Dictionary in Sparse Coding Edward J. Kim Connor Onweller Andrew O'Brien Kathleen F. McCoy 53 2 0 24 Nov 2020