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In decision support applications of AI, the AI algorithm's output is framed as a suggestion to a human user.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles. 1981 · 1981
Earlier work this paper cites.
Taking advice: Accepting help, improving judgment, and sharing responsibility
Nigel Harvey and Ilan Fischer. 1997 · 1997
Earlier work this paper cites.
Precision and accuracy of judgmental estimation
Ilan Yaniv and Dean P Foster. 1997 · 1997
Earlier work this paper cites.
Using advice and assessing its quality
Nigel Harvey, Clare Harries, and Ilan Fischer. 2000 · 2000
Earlier work this paper cites.
The perceived utility of human and automated aids in a visual detection task
Mary T Dzindolet, Linda G Pierce, Hall P Beck, and Lloyd A Dawe. 2002 · 2002
Earlier work this paper cites.
Effects of task difficulty on use of advice
Francesca Gino and Don A Moore. 2007 · 2007
Earlier work this paper cites.
Effects of information source, pedigree, and reliability on operator interaction with decision support systems
Poornima Madhavan and Douglas A Wiegmann. 2007 · 2007
Earlier work this paper cites.
The relative influence of advice from human experts and statistical methods on forecast adjustments
Dilek Önkal, Paul Goodwin, Mary Thomson, Sinan Gönül, and Andrew Pollock. 2009 · 2009
Earlier work this paper cites.
Random effects structure for testing interactions in linear mixed-effects models
Dale J Barr. 2013 · 2013
Earlier work this paper cites.
Cheap talk and credibility: The consequences of confidence and accuracy on advisor credibility and persuasiveness
Sunita Sah, Don A Moore, and Robert J MacCoun. 2013 · 2013
Earlier work this paper cites.
Fitting linear mixed-effects models using lme4
Douglas Bates, Martin Mächler, Ben Bolker, and Steve Walker. 2014 · 2014
Earlier work this paper cites.
jsPsych: A JavaScript library for creating behavioral experiments in a Web browser
Joshua R De Leeuw. 2015 · 2015
Cited alongside, same era.
Effects of distance between initial estimates and advice on advice utilization
Thomas Schultze, Anne-Fernandine Rakotoarisoa, and Stefan Schulz-Hardt. 2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . IEEE, 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
"Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . ACM, 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
UCI Machine Learning Repository
Dheeru Dua and Casey Graff. 2017 · 2017
Cited alongside, same era.
Global AI ethics: a review of the social impacts and ethical implications of artificial intelligence
Alexa Hagerty and Igor Rubinov. 2019 · 2019
Later among the works it cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
Later among the works it cites.
Following the Robot? Investigating Users’ Utilization of Advice from Robo-Advisors.. In ICIS . AIS
Christoph Tauchert, Neda Mesbah, et al · 2019
Later among the works it cites.
Why are we averse towards Algorithms? A comprehensive literature Review on Algorithm aversion.. In ECIS
Ekaterina Jussupow, Izak Benbasat, and Armin Heinzl. 2020 · 2020
Later among the works it cites.
CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems . ACM, 1–13
Yao Xie, Melody Chen, David Kao, Ge Gao, and Xiang’Anthony’ Chen. 2020 · 2020
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A large self-annotated corpus for sarcasm
Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2017 · 2017
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Understanding algorithm aversion: When is advice from automation discounted?
Andrew Prahl and Lyn Van Swol. 2017 · 2017
Cited alongside, same era.
A statistical approach to adult census income level prediction. In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) . IEEE, 207–212
Navoneel Chakrabarty and Sanket Biswas. 2018 · 2018
Cited alongside, same era.
Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management
Min Kyung Lee. 2018 · 2018
Cited alongside, same era.
Advice recipients: The psychology of advice utilization
Lyn M Van Swol, Jihyun Esther Paik, and Andrew Prahl. 2018 · 2018
Cited alongside, same era.
Artificial intelligence in the health care space: how we can trust what we cannot know
Robin C Feldman, Ehrik Aldana, and Kara Stein. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Do as AI say: susceptibility in deployment of clinical decision-aids
Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J Berkowitz, Eva Lermer, Joseph F Coughlin, John V Guttag, Errol Colak, and Marzyeh Ghassemi. 2021 · 2021
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Whose Advice Counts More–Man or Machine? An Experimental Investigation of AI-based Advice Utilization. In Proceedings of the 54th Hawaii International Conference on System Sciences . ScholarSpace, 4083
Neda Mesbah, Christoph Tauchert, and Peter Buxmann. 2021 · 2021
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A patient-centric dataset of images and metadata for identifying melanomas using clinical context
Veronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, Liam Caffery, Emmanouil Chousakos, Noel Codella, Marc Combalia, Stephen Dusza, Pascale Guitera, David Gutman, et al · 2021
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