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A growing literature studies how humans incorporate advice from algorithms.
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini and Yosef Hochberg · 1995
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The nonparametric behrens-fisher problem: asymptotic theory and a small-sample approximation
Edgar Brunner and Ullrich Munzel · 2000
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Trust, confidence, and expertise in a judge-advisor system
Janet A Sniezek and Lyn M Van Swol · 2001
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Effects of task difficulty on use of advice
Francesca Gino and Don A Moore · 2007
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Overconfidence and underconfidence: When and why people underestimate (and overestimate) the competition
Don A Moore and Daylian M Cain · 2007
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Performance targets and the brier score
Mark S Roulston · 2007
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The trouble with overconfidence
Don A Moore and Paul J Healy · 2008
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Effects of questionnaire length on participation and indicators of response quality in a web survey
Mirta Galesic and Michael Bosnjak · 2009
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Use of brier score to assess binary predictions
Kaspar Rufibach · 2010
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Algorithm aversion: people erroneously avoid algorithms after seeing them err
Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey · 2015
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Too much humanness for human-robot interaction: exposure to highly humanlike robots elicits aversive responding in observers
Megan Strait, Lara Vujovic, Victoria Floerke, Matthias Scheutz, and Heather Urry · 2015
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them
Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey · 2018
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Why trust an algorithm? performance, cognition, and neurophysiology
Veronika Alexander, Collin Blinder, and Paul J Zak · 2018
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Cheating is in the eye of the beholder: An evolving understanding of academic misconduct
Kyle A Burgason, Ophir Sefiha, and Lisa Briggs · 2019
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Algorithm appreciation: People prefer algorithmic to human judgment
Jennifer M Logg, Julia A Minson, and Don A Moore · 2019
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Task-dependent algorithm aversion
Noah Castelo, Maarten W Bos, and Donald R Lehmann · 2019
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Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases
Xueming Luo, Siliang Tong, Zheng Fang, and Zhe Qu · 2019
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Shalini Ghosh, Giedrius Burachas, Arijit Ray, and Avi Ziskind · 2019
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
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Why are we averse towards algorithms? a comprehensive literature review on algorithm aversion
Ekaterina Jussupow, Izak Benbasat, and Armin Heinzl · 2020
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A systematic review of algorithm aversion in augmented decision making
Jason W Burton, Mari-Klara Stein, and Tina Blegind Jensen · 2020
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Rapid trust calibration through interpretable and uncertainty-aware ai
Richard Tomsett, Alun Preece, Dave Braines, Federico Cerutti, Supriyo Chakraborty, Mani Srivastava, Gavin Pearson, and Lance Kaplan · 2020
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Calibrating human-ai collaboration: Impact of risk, ambiguity and transparency on algorithmic bias
Philipp Schmidt and Felix Biessmann · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Long-term video question answering via multimodal hierarchical memory attentive networks
Ting Yu, Jun Yu, Zhou Yu, Qingming Huang, and Qi Tian · 2020
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Language models as or for knowledge bases
Simon Razniewski, Andrew Yates, Nora Kassner, and Gerhard Weikum · 2021
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Who is the expert? reconciling algorithm aversion and algorithm appreciation in ai-supported decision making
Yoyo Tsung-Yu Hou and Malte F Jung · 2021
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Watch me improve—algorithm aversion and demonstrating the ability to learn
Benedikt Berger, Martin Adam, Alexander Rühr, and Alexander Benlian · 2021
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Towards a better understanding on mitigating algorithm aversion in forecasting: An experimental study
Markus Jung and Mischa Seiter · 2021
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Estimating the impact of “humanizing” customer service chatbots
Scott Schanke, Gordon Burtch, and Gautam Ray · 2021
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Humans rely more on algorithms than social influence as a task becomes more difficult
Eric Bogert, Aaron Schecter, and Richard T Watson · 2021
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Algorithm appreciation or aversion? comparing in-service and pre-service teachers’ acceptance of computerized expert models
Esther Kaufmann · 2021
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Reducing algorithm aversion through experience
Ibrahim Filiz, Jan René Judek, Marco Lorenz, and Markus Spiwoks · 2021
Earlier work this paper cites.
Daehwan Ahn, Abdullah Almaatouq, Monisha Gulabani, and Kartik Hosanagar · 2021
Cited alongside, same era.
Explainable ai and adoption of financial algorithmic advisors: an experimental study
Daniel Ben David, Yehezkel S Resheff, and Talia Tron · 2021
Cited alongside, same era.
How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig · 2021
Cited alongside, same era.
Strategic and Adaptive Behaviours in Trust Systems
Taha Gunes et al · 2021
Cited alongside, same era.
Alignment problems with current forecasting platforms
Nuño Sempere and Alex Lawsen · 2021
Cited alongside, same era.
How does chatgpt perform on the united states medical licensing examination? the implications of large language models for medical education and knowledge assessment
Aidan Gilson, Conrad W Safranek, Thomas Huang, Vimig Socrates, Ling Chi, Richard Andrew Taylor, David Chartash, et al · 2023
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Chatgpt passing usmle shines a spotlight on the flaws of medical education, 2023
Amarachi B Mbakwe, Ismini Lourentzou, Leo Anthony Celi, Oren J Mechanic, and Alon Dagan · 2023
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Can chatgpt pass the life support exams without entering the american heart association course?
Nino Fijačko, Lucija Gosak, Gregor Štiglic, Christopher T Picard, and Matthew John Douma · 2023
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Chatgpt outscored human candidates in a virtual objective structured clinical examination (osce) in obstetrics and gynecology
Matthew W Kemp, Susan JS Logan, Pooja Sharma Dimri, Navkaran Singh, Citra NZ Mattar, Pradip Dashraath, Harshaana Ramlal, Aniza P Mahyuddin, Suren Kanayan, Sean WD Carter, et al · 2023
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Could an artificial-intelligence agent pass an introductory physics course?
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ireason: Multimodal commonsense reasoning using videos and natural language with interpretability
Aman Chadha and Vinija Jain · 2021
Cited alongside, same era.
Chatgpt: The end of online exam integrity?
Teo Susnjak · 2022
Cited alongside, same era.
How to overcome algorithm aversion: Learning from mistakes
Taly Reich, Alex Kaju, and Sam J Maglio · 2022
Cited alongside, same era.
Preference for human, not algorithm aversion
Carey K Morewedge · 2022
Cited alongside, same era.
When self-humanization leads to algorithm aversion: what users want from decision support systems on prosocial microlending platforms
Pascal Oliver Heßler, Jella Pfeiffer, and Sebastian Hafenbrädl · 2022
Cited alongside, same era.
Are hard examples also harder to explain? a study with human and model-generated explanations
Swarnadeep Saha, Peter Hase, Nazneen Rajani, and Mohit Bansal · 2022
Cited alongside, same era.
Algorithmic versus human advice: Does presenting prediction performance matter for algorithm appreciation?
Sangseok You, Cathy Liu Yang, and Xitong Li · 2022
Cited alongside, same era.
Gerd Kortemeyer · 2023
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What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education
Ahmed Tlili, Boulus Shehata, Michael Agyemang Adarkwah, Aras Bozkurt, Daniel T Hickey, Ronghuai Huang, and Brighter Agyemang · 2023
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Chatting and cheating: Ensuring academic integrity in the era of chatgpt
Debby RE Cotton, Peter A Cotton, and J Reuben Shipway · 2023
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Chatgpt utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns
Malik Sallam · 2023
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How chat gpt can transform autodidactic experiences and open education
Mehmet Firat · 2023
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Chatgpt: A meta-analysis after 2.5 months
Christoph Leiter, Ran Zhang, Yanran Chen, Jonas Belouadi, Daniil Larionov, Vivian Fresen, and Steffen Eger · 2023
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“so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy
Yogesh K Dwivedi, Nir Kshetri, Laurie Hughes, Emma Louise Slade, Anand Jeyaraj, Arpan Kumar Kar, Abdullah M Baabdullah, Alex Koohang, Vishnupriya Raghavan, Manju Ahuja, et al · 2023
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Should artificial intelligent agents be your co-author? arguments in favour, informed by chatgpt, 2023
Michael Jay Polonsky and Jeffrey D Rotman · 2023
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Is using chatgpt cheating, plagiarism, both, neither, or forward thinking?
Brent A Anders · 2023
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A novel approach to generate distractors for multiple choice questions
Archana Praveen Kumar, Ashalatha Nayak, Manjula Shenoy, Shashank Goyal, et al · 2023
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Chatgpt performance on mcq-based exams
Philip Mark Newton · 2023
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On chatgpt: what promise remains for multiple choice assessment?
Chahna Gonsalves · 2023
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Seeing chatgpt through students’ eyes: An analysis of tiktok data
Anna-Carolina Haensch, Sarah Ball, Markus Herklotz, and Frauke Kreuter · 2023
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Is algorithm aversion weird? a cross-country comparison of individual-differences and algorithm aversion
Nicole Tsz Yeung Liu, Samuel N. Kirshner, and Eric T.K. Lim · 2023
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The effect of interpretable artificial intelligence on repeated managerial decision-making under uncertainty
Onur Altintas, Abraham Seidmann, and Bin Gu · 2023
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Non-task expert physicians benefit from correct explainable ai advice when reviewing x-rays
Susanne Gaube, Harini Suresh, Martina Raue, Eva Lermer, Timo K Koch, Matthias FC Hudecek, Alun D Ackery, Samir C Grover, Joseph F Coughlin, Dieter Frey, et al · 2023
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The risks of using chatgpt to obtain common safety-related information and advice
Oscar Oviedo-Trespalacios, Amy E Peden, Thomas Cole-Hunter, Arianna Costantini, Milad Haghani, Sage Kelly, Helma Torkamaan, Amina Tariq, James David Albert Newton, Timothy Gallagher, et al · 2023
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Chatgpt and antimicrobial advice: the end of the consulting infection doctor?
Alex Howard, William Hope, and Alessandro Gerada · 2023
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Aesthetic surgery advice and counseling from artificial intelligence: A rhinoplasty consultation with chatgpt
Yi Xie, Ishith Seth, David J Hunter-Smith, Warren M Rozen, Richard Ross, and Matthew Lee · 2023
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Does chatgpt provide appropriate and equitable medical advice?: A vignette-based, clinical evaluation across care contexts
Anthony J Nastasi, Katherine R Courtright, Scott D Halpern, and Gary E Weissman · 2023
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A review of chatgpt ai’s impact on several business sectors
A Shaji George and AS Hovan George · 2023
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Corrupted by algorithms? how ai-generated and human-written advice shape (dis) honesty
Margarita Leib, Nils Köbis, Rainer Michael Rilke, Marloes Hagens, and Bernd Irlenbusch · 2023
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Chatgpt’s inconsistent moral advice influences users’ judgment
Sebastian Krügel, Andreas Ostermaier, and Matthias Uhl · 2023
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Ai model gpt-3 (dis) informs us better than humans
Giovanni Spitale, Nikola Biller-Andorno, and Federico Germani · 2023
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Improved trust in human-robot collaboration with chatgpt
Yang Ye, Hengxu You, and Jing Du · 2023
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Content beats competence: People devalue chatgpt’s perceived competence but not its recommendations
Robert Böhm, Moritz Jörling, Leonhard Reiter, and Christoph Fuchs · 2023
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Investigating the impact of user trust on adoption and use of chatgpt: A survey analysis
Avishek Choudhury and Hamid Shamszare · 2023
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Enhancing chain-of-thoughts prompting with iterative bootstrapping in large language models
Jiashuo Sun, Yi Luo, Yeyun Gong, Chen Lin, Yelong Shen, Jian Guo, and Nan Duan · 2023
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Economics of chatgpt: A labor market view on the occupational impact of artificial intelligence
Ali Zarifhonarvar · 2023
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Gpts are gpts: An early look at the labor market impact potential of large language models
Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock · 2023
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