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Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations.
Quantifying Interpretability and Trust in Machine Learning Systems
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Why and Why Not Explanations Improve the Intelligibility of Context-aware Intelligent Systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI ’09) . ACM, New York, NY, USA, 2119–2128
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Julian McAuley, Jure Leskovec, and Dan Jurafsky. 2012 · 2012
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Decisions with uncertainty: The glass half full
Susan Joslyn and Jared LeClerc. 2013 · 2013
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Data-Driven Decisions for Reducing Readmissions for Heart Failure: General Methodology and Case Study
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Truth-default theory (TDT) a theory of human deception and deception detection
Timothy R Levine. 2014 · 2014
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The role of explanations on trust and reliance in clinical decision support systems. In 2015 International Conference on Healthcare Informatics . IEEE, IEEE, Dallas, Texas, 160–169
Adrian Bussone, Simone Stumpf, and Dympna O’Sullivan. 2015 · 2015
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Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-Day Readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Sydney, NSW, Australia) (KDD ’15) . Association for Computing Machinery, New York, NY, USA, 1721–1730
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Elena L Glassman, Jeremy Scott, Rishabh Singh, Philip J Guo, and Robert C Miller. 2015 · 2015
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Incentivizing High Quality Crowdwork. In Proceedings of the 24th International Conference on World Wide Web (Florence, Italy) (WWW ’15) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 419–429
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, and Jennifer Wortman Vaughan. 2015 · 2015
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Machine bias: There’s software across the country to predict future criminals and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016 · 2016
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In Proceedings of the 25th International Conference on World Wide Web (Montréal, Québec, Canada) (WWW ’16) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 507–517
Ruining He and Julian McAuley. 2016 · 2016
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Effective Crowd Annotation for Relation Extraction. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, San Diego, California, 897–906
Angli Liu, Stephen Soderland, Jonathan Bragg, Christopher H. Lin, Xiao Ling, and Daniel S. Weld. 2016 · 2016
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Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff
Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S. Weld, Walter S. Lasecki, and Eric Horvitz. 2019b · 2019
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" Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making
Carrie J Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019 · 2019
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What Can AI Do for Me? Evaluating Machine Learning Interpretations in Cooperative Play. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19) . Association for Computing Machinery, New York, NY, USA, 229–239
Shi Feng and Jordan Boyd-Graber. 2019 · 2019
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The principles and limits of algorithm-in-the-loop decision making
Ben Green and Yiling Chen. 2019 · 2019
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Does transparency in moderation really matter? User behavior after content removal explanations on reddit
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Attention-based LSTM for Aspect-level Sentiment Classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Austin, Texas, 606–615
Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016 · 2016
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Patient risk stratification with time-varying parameters: a multitask learning approach
Jenna Wiens, John Guttag, and Eric Horvitz. 2016 · 2016
Cited alongside, same era.
On Calibration of Modern Neural Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML’17) . JMLR.org, Sydney, NSW, Australia, 1321–1330
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
Cited alongside, same era.
Can AI Become Reliable Source to Support Human Decision Making in a Court Scene?. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW ’17 Companion) . Association for Computing Machinery, New York, NY, USA, 195–198
Yugo Hayashi and Kosuke Wakabayashi. 2017 · 2017
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. In 5th International Conference on Learning Representations, ICLR 2017 . OpenReview.net, Toulon, France, 1–12
Dan Hendrycks and Kevin Gimpel. 2017 · 2017
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Understanding Black-Box Predictions via Influence Functions. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML’17) . JMLR.org, Sydney, NSW, Australia, 1885–1894
Pang Wei Koh and Percy Liang. 2017 · 2017
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A Structured Self-Attentive Sentence Embedding. In 5th International Conference on Learning Representations, ICLR 2017, Conference Track Proceedings . OpenReview.net, Toulon, France, 1–15
Zhouhan Lin, Minwei Feng, Cícero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
LSAT prep book study guide: quick study & practice test questions for the Law School Admissions council’s (LSAC) Law school admission test
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Shagun Jhaver, Amy Bruckman, and Eric Gilbert. 2019 · 2019
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Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–12
Johannes Kunkel, Tim Donkers, Lisa Michael, Catalin-Mihai Barbu, and Jürgen Ziegler. 2019 · 2019
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On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 29–38
Vivian Lai and Chenhao Tan. 2019 · 2019
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Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 220–229
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
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A Slow Algorithm Improves Users’ Assessments of the Algorithm’s Accuracy
Joon Sung Park, Rick Barber, Alex Kirlik, and Karrie Karahalios. 2019 · 2019
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Finding Generalizable Evidence by Learning to Convince Q&A Models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Association for Computational Linguistics, Hong Kong, China, 2402–2411
Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, and Kyunghyun Cho. 2019 · 2019
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Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2019 · 2019
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Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–15
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019 · 2019
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The Challenge of Crafting Intelligible Intelligence
Daniel S. Weld and Gagan Bansal. 2019 · 2019
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Do I Trust My Machine Teammate? An Investigation from Perception to Decision. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19) . Association for Computing Machinery, New York, NY, USA, 460–468
Kun Yu, Shlomo Berkovsky, Ronnie Taib, Jianlong Zhou, and Fang Chen. 2019 · 2019
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Explainable Machine Learning in Deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20) . Association for Computing Machinery, New York, NY, USA, 648–657
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020 · 2020
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Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems. In Proceedings of the 25th International Conference on Intelligent User Interfaces (Cagliari, Italy) (IUI ’20) . Association for Computing Machinery, New York, NY, USA, 454–464
Zana Buçinca, Phoebe Lin, Krzysztof Z. Gajos, and Elena L. Glassman. 2020 · 2020
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Mental Models of AI Agents in a Cooperative Game Setting. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20) . Association for Computing Machinery, New York, NY, USA, 1–12
Katy Ilonka Gero, Zahra Ashktorab, Casey Dugan, Qian Pan, James Johnson, Werner Geyer, Maria Ruiz, Sarah Miller, David R. Millen, Murray Campbell, Sadhana Kumaravel, and Wei Zhang. 2020 · 2020
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Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Online, 5540–5552
Peter Hase and Mohit Bansal. 2020 · 2020
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20) . Association for Computing Machinery, New York, NY, USA, 1–14
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan. 2020 · 2020
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"Why is ’Chicago’ Deceptive?" Towards Building Model-Driven Tutorials for Humans. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20) . Association for Computing Machinery, New York, NY, USA, 1–13
Vivian Lai, Han Liu, and Chenhao Tan. 2020 · 2020
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Consistent Estimators for Learning to Defer to an Expert. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, Virtual, 7076–7087
Hussein Mozannar and David Sontag. 2020 · 2020
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Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20) . Association for Computing Machinery, New York, NY, USA, 1–14
C. Estelle Smith, Bowen Yu, Anjali Srivastava, Aaron Halfaker, Loren Terveen, and Haiyi Zhu. 2020 · 2020
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Learning to Complement Humans. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 , Christian Bessiere (Ed.). International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan, 1526–1533
Bryan Wilder, Eric Horvitz, and Ece Kamar. 2020 · 2020
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How Do Visual Explanations Foster End Users’ Appropriate Trust in Machine Learning?. In Proceedings of the 25th International Conference on Intelligent User Interfaces (Cagliari, Italy) (IUI ’20) . Association for Computing Machinery, New York, NY, USA, 189–201
Fumeng Yang, Zhuanyi Huang, Jean Scholtz, and Dustin L. Arendt. 2020 · 2020
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ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning. In 8th International Conference on Learning Representations, ICLR 2020 . OpenReview.net, Addis Ababa, Ethiopia, 1–26
Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng. 2020 · 2020
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Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20) . Association for Computing Machinery, New York, NY, USA, 295–305
Yunfeng Zhang, Q. Vera Liao, and Rachel K. E. Bellamy. 2020 · 2020
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Natural Language Generation enhances human decision-making with uncertain information. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Association for Computational Linguistics, Berlin, Germany, 264–268
Dimitra Gkatzia, Oliver Lemon, and Verena Rieser. 2016 · 2043
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