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Research in human-centered AI has shown the benefits of systems that can explain their predictions.
Programs with common sense
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SPECTER: Document-level Representation Learning using Citation-informed Transformers
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Document Classification Through Interactive Supervision of Document and Term Labels. In PKDD ’04 , Jean-François Boulicaut, Floriana Esposito, Fosca Giannotti, and Dino Pedreschi (Eds.). 185–196
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Boosting with prior knowledge for call classification
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Trust Building with Explanation Interfaces. In IUI ’06 (Sydney, AU). ACM, 93–100
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Open User Profiles for Adaptive News Systems: Help or Harm?. In WWW ’07 (Banff, Alberta, Canada). ACM, 11–20
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A Survey of Explanations in Recommender Systems. In 2007 IEEE 23rd International Conference on Data Engineering Workshop . 801–810
Nava Tintarev and Judith Masthoff. 2007 · 2007
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The effects of transparency on trust in and acceptance of a content-based art recommender
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Learning from Labeled Features Using Generalized Expectation Criteria. In SIGIR ’08 (Singapore, Singapore). 595–602
Gregory Druck, Gideon Mann, and Andrew McCallum. 2008 · 2008
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PeerChooser: Visual Interactive Recommendation. In CHI ’08 (Florence, Italy). ACM, 1085–1088
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ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009 · 2009
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SmallWorlds: Visualizing Social Recommendations
Brynjar Gretarsson, John O’Donovan, Svetlin Bostandjiev, Christopher Hall, and Tobias Höllerer. 2010 · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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Automatically Building Research Reading Lists. In RecSys ’10 (Barcelona, Spain). ACM, 159–166
Michael Ekstrand, Praveen Kannan, James Stemper, John Butler, Joseph Konstan, and John Riedl. 2010 · 2010
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Context-aware Citation Recommendation. In WWW ’10 (Raleigh, USA). ACM, 421–430
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Towards Maximizing the Accuracy of Human-Labeled Sensor Data. In IUI ’10 . 259–268
Stephanie Rosenthal and Anind Dey. 2010 · 2010
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Each to His Own: How Different Users Call for Different Interaction Methods in Recommender Systems. In RecSys ’11 (Chicago, USA). ACM, 141–148
Bart Knijnenburg, Niels Reijmer, and Martijn Willemsen. 2011 · 2011
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Scikit-learn: Machine Learning in Python
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A User-centric Evaluation Framework for Recommender Systems. In RecSys ’11 (Chicago, USA). ACM, 157–164
Pearl Pu, Li Chen, and Rong Hu. 2011 · 2011
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Designing and Evaluating Explanations for Recommender Systems
Nava Tintarev and Judith Masthoff. 2011 · 2011
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Regroup: Interactive Machine Learning for On-demand Group Creation in Social Networks. In CHI ’12 (Austin, USA). ACM, 21–30
Saleema Amershi, James Fogarty, and Daniel Weld. 2012 · 2012
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TasteWeights: A Visual Interactive Hybrid Recommender System. In RecSys ’12 (Dublin, Ireland). ACM, 35–42
Svetlin Bostandjiev, John O’Donovan, and Tobias Höllerer. 2012 · 2012
Cited alongside, same era.
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent. In CHI ’12 (Austin, USA). ACM, 1
Todd Kulesza, Simone Stumpf, Margaret Burnett, and Irwin Kwan. 2012 · 2012
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Intelligible Models for Classification and Regression. In KDD ’12 (Beijing, China). ACM, 150–158
Yin Lou, Rich Caruana, and Johannes Gehrke. 2012 · 2012
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The Tag Genome: Encoding Community Knowledge to Support Novel Interaction
Jesse Vig, Shilad Sen, and John Riedl. 2012 · 2012
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An Approach to Controlling User Models and Personalization Effects in Recommender Systems. In IUI ’13 (Santa Monica, CA). ACM, 49–56
Fedor Bakalov, Marie-Jean Meurs, Birgitta König-Ries, Bahar Sateli, René Witte, Greg Butler, and Adrian Tsang. 2013 · 2013
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. In IJCAI ’17 . 2662–2670
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez. 2017 · 2017
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Machine Teaching: A New Paradigm for Building Machine Learning Systems
Patrice Simard, Saleema Amershi, David Chickering, Alicia Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, and John Wernsing. 2017 · 2017
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Construction of the Literature Graph in Semantic Scholar. In NAACL-HLT ’18 (New Orleans, USA). ACL, 84–91
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Content-Based Citation Recommendation. In ACL ’18 . ACL, New Orleans, LA, 238–251
Chandra Bhagavatula, Sergey Feldman, Russell Power, and Waleed Ammar. 2018 · 2018
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A Survey of Methods for Explaining Black Box Models
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Cited alongside, same era.
LinkedVis: exploring social and semantic career recommendations. In IUI ’13 (Santa Monica, USA). ACM, 107
Svetlin Bostandjiev, John O’Donovan, and Tobias Höllerer. 2013 · 2013
Cited alongside, same era.
End-User Feature Labeling: Supervised and Semi-Supervised Approaches Based on Locally-Weighted Logistic Regression
Shubhomoy Das, Travis Moore, Weng-Keen Wong, Simone Stumpf, Ian Oberst, Kevin Mcintosh, and Margaret Burnett. 2013 · 2013
Cited alongside, same era.
Too much, too little, or just right? Ways explanations impact end users’ mental models. In VL/HCC ’13 . 3–10
Todd Kulesza, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng-Keen Wong. 2013 · 2013
Cited alongside, same era.
Accurate Intelligible Models with Pairwise Interactions. In KDD ’13 (Chicago, USA). ACM, 623–631
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker. 2013 · 2013
Cited alongside, same era.
Visualizing Recommendations to Support Exploration, Transparency and Controllability. In IUI ’13 (Santa Monica, USA). ACM, 351–362
Katrien Verbert, Denis Parra, Peter Brusilovsky, and Erik Duval. 2013 · 2013
Cited alongside, same era.
Power to the People: The Role of Humans in Interactive Machine Learning
Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014 · 2014
Cited alongside, same era.
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. 2014 · 2014
Cited alongside, same era.
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018 · 2018
Later among the works it cites.
Effects of personal characteristics on music recommender systems with different levels of controllability. In RecSys ’18 (Vancouver, Canada). ACM, 13–21
Yucheng Jin, Nava Tintarev, and Katrien Verbert. 2018 · 2018
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R: A Language and Environment for Statistical Computing
R Core Team. 2018 · 2018
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Reinforcement learning: an introduction (second edition ed.)
Richard S. Sutton and Andrew G. Barto. 2018 · 2018
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Beyond the Ranked List: User-Driven Exploration and Diversification of Social Recommendation. In IUI ’18 (Tokyo, Japan). ACM, 239–250
Chun-Hua Tsai and Peter Brusilovsky. 2018 · 2018
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Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang and Xu Chen. 2018 · 2018
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Guidelines for Human-AI Interaction. In CHI ’19 (Glasgow, Scotland). ACM, Article 3
Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul Bennett, Kori Inkpen, and et al. 2019 · 2019
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SciBERT: A Pretrained Language Model for Scientific Text. In EMNLP ’19
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
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Teaching a black-box learner. In ICML ’19 . PMLR, Long Beach, USA, 1547–1555
Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, and Xiaojin Zhu. 2019 · 2019
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A Scalable Hybrid Research Paper Recommender System for Microsoft Academic. In WWW’ 19 (San Francisco, USA). ACM, 2893–2899
Anshul Kanakia, Zhihong Shen, Darrin Eide, and Kuansan Wang. 2019 · 2019
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Incorporating Priors with Feature Attribution on Text Classification. In ACL ’19 (Florence, Italy). ACL, 6274–6283
Frederick Liu and Besim Avci. 2019 · 2019
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Explaining Recommendations in an Interactive Hybrid Social Recommender. In IUI ’19 (Marina del Ray, USA). ACM, 391–396
Chun-Hua Tsai and Peter Brusilovsky. 2019 · 2019
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Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering
Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019 · 2019
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The Challenge of Crafting Intelligible Intelligence
Daniel Weld and Gagan Bansal. 2019 · 2019
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Local Decision Pitfalls in Interactive Machine Learning: An Investigation into Feature Selection in Sentiment Analysis
Tongshuang Wu, Daniel S. Weld, and Jeffrey Heer. 2019 · 2019
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Rewriting a Deep Generative Model. In Proceedings of the European Conference on Computer Vision (ECCV)
David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, and Antonio Torralba. 2020 · 2020
Closest in time.
Interfaces and Human Decision Making for Recommender Systems. In RecSys ’20 (Virtual Event, Brazil). 613–618
Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O’Donovan, Giovanni Semeraro, and Martijn C. Willemsen. 2020 · 2020
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Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020 · 2020
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Compositional Explanations of Neurons. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 17153–17163
Jesse Mu and Jacob Andreas. 2020 · 2020
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Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge. 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, 8116–8126
Laura Rieger, Chandan Singh, William Murdoch, and Bin Yu. 2020 · 2020
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No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML
Alison Smith-Renner, Ron Fan, M. Keith Birchfield, Tongshuang Sherry Wu, Jordan L. Boyd-Graber, Daniel S. Weld, and Leah Findlater. 2020 · 2020
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The effects of controllability and explainability in a social recommender system
Chun-Hua Tsai and Peter Brusilovsky. 2020 · 2020
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GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie Jay Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, and Rich Caruana. 2021 · 2021
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Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems
Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, and Lin Guan. 2022 · 2022
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Exploring the Role of Local and Global Explanations in Recommender Systems. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22) . Association for Computing Machinery, New York, NY, USA, Article 290, 7 pages
Marissa Radensky, Doug Downey, Kyle Lo, Zoran Popovic, and Daniel S Weld. 2022 · 2022
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Leveraging Explanations in Interactive Machine Learning: An Overview
Stefano Teso, Öznur Alkan, Wolfang Stammer, and Elizabeth Daly. 2022 · 2022
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