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The human-centric explainable artificial intelligence (HCXAI) community has raised the need for framing the explanation process as a conversation between human and machine.
Generating interactive explanations
Alison Cawsey · 1991
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Goal-based explanation evaluation
David B. Leake · 1991
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Planning text for advisory dialogues: Capturing intentional and rhetorical information
Johanna D. Moore and Cecile L. Paris · 1993
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Commitment in Dialogue: Basic Concepts of Interpersonal Reasoning
Douglas N. Walton and Erik C.W. Krabbe · 1995
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Simplicity and probability in causal explanation
Tania Lombrozo · 2007
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Assessing demand for intelligibility in context-aware applications
Brian Y. Lim and Anind K. Dey · 2009
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Interacting meaningfully with machine learning systems: Three experiments
Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret Burnett, Thomas Dietterich, Erin Sullivan, and Jonathan Herlocker · 2009
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Principles of explanatory debugging to personalize interactive machine learning
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf · 2015
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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The promise and peril of human evaluation for model interpretability
Bernease Herman · 2017
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Social attribution and explanation
Denis Hilton · 2017
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Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda
Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli · 2018
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom · 2018
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QuAC: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer · 2018
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Towards making NLG a voice for interpretable machine learning
James Forrest, Somayajulu Sripada, Wei Pang, and George Coghill · 2018
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Explaining explanations: An overview of interpretability of machine learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C. Lipton · 2018
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Analyzing and characterizing user intent in information-seeking conversations
Chen Qu, Liu Yang, W. Bruce Croft, Johanne R. Trippas, Yongfeng Zhang, and Minghui Qiu · 2018
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Interpretation of natural language rules in conversational machine reading
Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, and Sebastian Riedel · 2018
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Going beyond visualization : Verbalization as complementary medium to explain machine learning models
Rita Sevastjanova, Fabian Beck, Basil Ell, Cagatay Turkay, Rafael Henkin, Miriam Butt, Daniel A. Keim, and Mennatallah El-Assady · 2018
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Natural language interaction with explainable ai models
Arjun R Akula, Sinisa Todorovic, Joyce Y Chai, and Song-Chun Zhu · 2019
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Weight of evidence as a basis for human-oriented explanations
David Alvarez-Melis, Hal Daumé III, Jennifer Wortman Vaughan, and Hanna M. Wallach · 2019
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From explainability to explanation: Using a dialogue setting to elicit annotations with justifications
Nazia Attari, Martin Heckmann, and David Schlangen · 2019
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Automated rationale generation: A technique for explainable ai and its effects on human perceptions
Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, and Mark O. Riedl · 2019
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Gamut: A design probe to understand how data scientists understand machine learning models
Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, and Steven M. Drucker · 2019
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A grounded interaction protocol for explainable artificial intelligence
Prashan Madumal, Tim Miller, Liz Sonenberg, and Frank Vetere · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Introducing mantis: a novel multi-domain information seeking dialogues dataset
Gustavo Penha, Alexandru Balan, and Claudia Hauff · 2019
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Natural language generation challenges for explainable AI
Ehud Reiter · 2019
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Can we do better explanations? a proposal of user-centered explainable ai
Mireia Ribera and Agata Lapedriza · 2019
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AllenNLP interpret: A framework for explaining predictions of NLP models
Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, and Sameer Singh · 2019
Cited alongside, same era.
The challenge of crafting intelligible intelligence
Daniel S. Weld and Gagan Bansal · 2019
Cited alongside, same era.
ERASER: A benchmark to evaluate rationalized NLP models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace · 2020
Cited alongside, same era.
Human-centered explainable ai: Towards a reflective sociotechnical approach
Upol Ehsan and Mark O. Riedl · 2020
Cited alongside, same era.
doc2dial: A goal-oriented document-grounded dialogue dataset
Song Feng, Hui Wan, Chulaka Gunasekara, Siva Patel, Sachindra Joshi, and Luis Lastras · 2020
Cited alongside, same era.
Explainable active learning (XAL): an empirical study of how local explanations impact annotator experience
Question-driven design process for explainable AI user experiences
Q. Vera Liao, Milena Pribic, Jaesik Han, Sarah Miller, and Daby Sow · 2021
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Post-hoc interpretability for neural nlp: A survey
Andreas Madsen, Siva Reddy, and A. P. Sarath Chandar · 2021
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A multidisciplinary survey and framework for design and evaluation of explainable ai systems
Sina Mohseni, Niloofar Zarei, and Eric D. Ragan · 2021
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Towards an explainer-agnostic conversational xai
Navid Nobani, Fabio Mercorio, and Mario Mezzanzanica · 2021
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Xalgo: A design probe of explaining algorithms’ internal states via question-answering
Juan Rebanal, Jordan Combitsis, Yuqi Tang, and Xiang ’Anthony’ Chen · 2021
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Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, and Klaus Mueller · 2020
Cited alongside, same era.
Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal · 2020
Cited alongside, same era.
Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language?
Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal · 2020
Cited alongside, same era.
Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, T. J. Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeff Wu, and Dario Amodei · 2020
Cited alongside, same era.
Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson · 2020
Cited alongside, same era.
ALICE: Active learning with contrastive natural language explanations
Weixin Liang, James Zou, and Zhou Yu · 2020
Cited alongside, same era.
LMdiff: A visual diff tool to compare language models
Hendrik Strobelt, Benjamin Hoover, Arvind Satyanaryan, and Sebastian Gehrmann · 2021
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Rationales for sequential predictions
Keyon Vafa, Yuntian Deng, David Blei, and Alexander Rush · 2021
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Challenges in designing natural language interfaces for complex visual models
Henrik Voigt, Monique Meuschke, Kai Lawonn, and Sina Zarrieß · 2021
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“it’s our fault!”: Insights into users’ understanding and interaction with an explanatory collaborative dialog system
Katharina Weitz, Lindsey Vanderlyn, Ngoc Thang Vu, and Elisabeth André · 2021
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Teach me to explain: A review of datasets for explainable nlp
Sarah Wiegreffe and Ana Marasović · 2021
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Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasović, and Noah A. Smith · 2021
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Refining language models with compositional explanations
Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, and Xiang Ren · 2021
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Explain, edit, and understand: Rethinking user study design for evaluating model explanations
Siddhant Arora, Danish Pruthi, Norman Sadeh, William W Cohen, Zachary C Lipton, and Graham Neubig · 2022
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Contextualization and exploration of local feature importance explanations to improve understanding and satisfaction of non-expert users
Clara Bove, Jonathan Aigrain, Marie-Jeanne Lesot, Charles Tijus, and Marcin Detyniecki · 2022
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Learning as conversation: Dialogue systems reinforced for information acquisition
Pengshan Cai, Hui Wan, Fei Liu, Mo Yu, Hong Yu, and Sachindra Joshi · 2022
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What to learn, and how: Toward effective learning from rationales
Samuel Carton, Surya Kanoria, and Chenhao Tan · 2022
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Dialog inpainting: Turning documents to dialogs
Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Mike Green, Qazi Rashid, and Kelvin Guu · 2022
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A survey on improving NLP models with human explanations
Mareike Hartmann and Daniel Sonntag · 2022
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When can models learn from explanations? A formal framework for understanding the roles of explanation data
Peter Hase and Mohit Bansal · 2022
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In-context learning for few-shot dialogue state tracking
Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, Tao Yu, Noah A. Smith, and Mari Ostendorf · 2022
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Diagnosing AI explanation methods with folk concepts of behavior
Alon Jacovi, Jasmijn Bastings, Sebastian Gehrmann, Yoav Goldberg, and Katja Filippova · 2022
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Rethinking explainability as a dialogue: A practitioner’s perspective
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, and Sameer Singh · 2022
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Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities
Mina Lee, Percy Liang, and Qian Yang · 2022
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Shikib Mehri, Jinho Choi, L. F. D’Haro, Jan Deriu, Maxine Eskénazi, Milica Gasic, Kallirroi Georgila, Dilek Z. Hakkani-Tür, Zekang Li, Verena Rieser, Samira Shaikh, David R. Traum, Yi-Ting Yeh, Zhou Yu, Yizhe Zhang, and Chen Zhang · 2022
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Red teaming language models with language models
Ethan Perez, Saffron Huang, H. Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving · 2022
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QA dataset explosion: A taxonomy of NLP resources for question answering and reading comprehension
Anna Rogers, Matt Gardner, and Isabelle Augenstein · 2022
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Human interpretation of saliency-based explanation over text
Hendrik Schuff, Alon Jacovi, Heike Adel, Yoav Goldberg, and Ngoc Thang Vu · 2022
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Genni: Human-ai collaboration for data-backed text generation
Hendrik Strobelt, Jambay Kinley, Robert Krueger, Johanna Beyer, Hanspeter Pfister, and Alexander M. Rush · 2022
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Reframing human-AI collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark O. Riedl, and Yejin Choi · 2022
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QAConv: Question answering on informative conversations
Chien-Sheng Wu, Andrea Madotto, Wenhao Liu, Pascale Fung, and Caiming Xiong · 2022
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Interpreting language models with contrastive explanations
Kayo Yin and Graham Neubig · 2022
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