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Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios.
Twelve-choice probability learning with payoffs
Forrest W Young. 1967 · 1967
Earlier work this paper cites.
The Academy of Management Review
D Harrison McKnight, Larry L Cummings, and Norman L Chervany. 1998 · 1998
Earlier work this paper cites.
Developing and validating trust measures for e-commerce: An integrative typology
D. Harrison McKnight, Vivek Choudhury, and Charles Kacmar. 2002 · 2002
Earlier work this paper cites.
Automation bias in intelligent time critical decision support systems. In Collection of Technical Papers - AIAA 1st Intelligent Systems Technical Conference , Vol. 2. 557–562
M. L. Cummings. 2004 · 2004
Earlier work this paper cites.
Type of automation failure: The effects on trust and reliance in automation. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting , Vol. 48. SAGE Publications Sage CA: Los Angeles, CA, 2163–2167
Jason D Johnson, Julian Sanchez, Arthur D Fisk, and Wendy A Rogers. 2004 · 2004
Earlier work this paper cites.
Trust in Automation: Designing for Appropriate Reliance
J. D. Lee and K. A. See. 2004 · 2004
Earlier work this paper cites.
Predicting Good Probabilities with Supervised Learning. In Proceedings of the 22Nd International Conference on Machine Learning (ICML ’05) . ACM, New York, NY, USA, 625–632
Alexandru Niculescu-Mizil and Rich Caruana. 2005 · 2005
Earlier work this paper cites.
Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces . ACM, 167–174
John O’Donovan and Barry Smyth. 2005 · 2005
Earlier work this paper cites.
Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information
John M. McGuirl and Nadine B. Sarter. 2006 · 2006
Earlier work this paper cites.
Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2013 . 210–217
Tove Helldin, Göran Falkman, Maria Riveiro, and Staffan Davidsson. 2013 · 2013
Earlier work this paper cites.
Algorithm aversion: People erroneously avoid algorithms after seeing them err
Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015 · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , Vol. 07-12-June-2015. IEEE Computer Society, 427–436
Anh Nguyen, Jason Yosinski, and Jeff Clune. 2015 · 2015
Cited alongside, same era.
Individual differences in the calibration of trust in automation
Vlad L. Pop, Alex Shrewsbury, and Francis T. Durso. 2015 · 2015
Cited alongside, same era.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Cited alongside, same era.
Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff. In AAAI Conference on Artificial Intelligence . AAAI
Gagan Bansal, Besmira Nushi, Ece Kamar, Dan Weld, Walter Lasecki, and Eric Horvitz. 2019 · 2019
Later among the works it cites.
The effects of example-based explanations in a machine learning interface. In International Conference on Intelligent User Interfaces, Proceedings IUI , Vol. Part F147615. Association for Computing Machinery, 258–262
Carrie J. Cai, Jonas Jongejan, and Jess Holbrook. 2019 · 2019
Later among the works it cites.
Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo V. Carvalho, Eduardo M. Pereira, and Jaime S. Cardoso. 2019 · 2019
Later among the works it cites.
Explaining decision-making algorithms through UI: Strategies to help non-expert stakeholders. In Conference on Human Factors in Computing Systems - Proceedings . Association for Computing Machinery
Hao Fei Cheng, Ruotong Wang, Zheng Zhang, Fiona O’Connell, Terrance Gray, F. Maxwell Harper, and Haiyi Zhu. 2019 · 2019
Later among the works it cites.
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UCI Machine Learning Repository
Dheeru Dua and Casey Graff. 2017 · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller. 2017 · 2017
Cited alongside, same era.
Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)
Amina Adadi and Mohammed Berrada. 2018 · 2018
Cited alongside, same era.
A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018 · 2018
Cited alongside, same era.
Metrics for explainable AI: Challenges and prospects
Robert R Hoffman, Shane T Mueller, Gary Klein, and Jordan Litman. 2018 · 2018
Cited alongside, same era.
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2018 · 2018
Cited alongside, same era.
Explaining models: An empirical study of how explanations impact fairness judgment. In International Conference on Intelligent User Interfaces, Proceedings IUI , Vol. Part F147615. Association for Computing Machinery, 275–285
Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang, Rachel K.E. Bellamy, and Casey Dugan. 2019 · 2019
Later among the works it cites.
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 . ACM, 487
Johannes Kunkel, Tim Donkers, Lisa Michael, Catalin-Mihai Barbu, and Jürgen Ziegler. 2019 · 2019
Later among the works it cites.
On Human Predictions with Explanations and Predictions of Machine Learning Models. In Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* ’19 . ACM Press, New York, New York, USA, 29–38
Vivian Lai and Chenhao Tan. 2019 · 2019
Later among the works it cites.
I can do better than your AI: Expertise and explanations. In International Conference on Intelligent User Interfaces, Proceedings IUI , Vol. Part F147615. Association for Computing Machinery, 240–251
James Schaffer, John O’Donovan, James Michaelis, Adrienne Raglin, and Tobias Höllerer. 2019 · 2019
Later among the works it cites.
Understanding the effect of accuracy on trust in machine learning models. In Conference on Human Factors in Computing Systems - Proceedings . Association for Computing Machinery
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach. 2019 · 2019
Later among the works it cites.
Do I trust my machine teammate? An investigation from perception to decision. In International Conference on Intelligent User Interfaces, Proceedings IUI , Vol. Part F147615. Association for Computing Machinery, 460–468
Kun Yu, Shlomo Berkovsky, Ronnie Taib, Jianlong Zhou, and Fang Chen. 2019 · 2019
Later among the works it cites.