Authors:
(1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu);
(2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Table of Links Abstract and Introduction
Methods
Results
Discussion
References
Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals References Agrawal, Ajay, Joshua Gans, and Avi Goldfarb (2019). The economics of artificial intelligence: an agenda. University of Chicago Press. Akram, Arslan (2023). “An Empirical Study of AI Generated Text Detection Tools”. arXiv preprint arXiv:2310.01423. Altmäe, Signe, Alberto Sola-Leyva, and Andres Salumets (2023). “Artificial intelligence in scientific writing: a friend or a foe?” Reproductive BioMedicine Online Athey, Susan and Guido W Imbens (2019). “Machine learning methods that economists should know about”. Annual Review of Economics 11, pp. 685–725. Beck, J Thaddeus et al. (2020). “Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center”. JCO clinical cancer informatics 4, pp. 50–59. Björkegren, Daniel, Joshua E. Blumenstock, and Samsun Knight (2022). (Machine) Learning What Policies Value. arXiv: 2206.00727 [econ.GN]. Bom, Hee-Seung Henry (2023). “Exploring the Opportunities and Challenges of ChatGPT in Academic Writing: a Roundtable Discussion”. Nuclear Medicine and Molecular Imaging, pp. 1– 3. Bommasani, Rishi et al. (2021). “On the opportunities and risks of foundation models”. arXiv preprint arXiv:2108.07258. Bringula, Rex (2023). “What do academics have to say about ChatGPT? A text mining analytics on the discussions regarding ChatGPT on research writing”. AI and Ethics, pp. 1–13. Capra, C. Monica, Matthew Gomies, and Shanshan Zhang (2023). “The Sound of Cooperation and Deception in High Stakes Interactions”. Charness, Gary, Brian Jabarian, and John A List (2023). Generation next: Experimentation with ai. Tech. rep. National Bureau of Economic Research. Chien, Chen-Fu et al. (2020). Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies. Cowen, Tyler and Alexander T Tabarrok (2023). “How to learn and teach economics with large language models, including GPT”. Including GPT (March 17, 2023). Daun, Marian and Jennifer Brings (2023). “How ChatGPT will change software engineering education”. In: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, pp. 110–116. Deza, Arturo, Amit Surana, and Miguel P Eckstein (2019). “Assessment of faster r-cnn in manmachine collaborative search”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3185–3194. Dietvorst, Berkeley J, Joseph P Simmons, and Cade Massey (2015). “Algorithm aversion: people erroneously avoid algorithms after seeing them err.” Journal of Experimental Psychology: General 144.1, p. 114. Dranove, David and Ginger Zhe Jin (2010). “Quality disclosure and certification: Theory and practice”. Journal of Economic Literature 48.4, pp. 935–63. Farrell, Max H, Tengyuan Liang, and Sanjog Misra (2020). “Deep learning for individual heterogeneity: An automatic inference framework”. arXiv preprint arXiv:2010.14694. Fitria, Tira Nur (2021). “Grammarly as AI-powered English writing assistant: Students’ alternative for writing English”. Metathesis: Journal of English Language, Literature, and Teaching 5.1, pp. 65–78. Franchi, Matt et al. (2023). “Detecting disparities in police deployments using dashcam data”. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 534–544. Fudenberg, Drew and Annie Liang (2019). “Predicting and understanding initial play”. American Economic Review 109.12, pp. 4112–4141 Fyfe, Paul (2023). “How to cheat on your final paper: Assigning AI for student writing”. AI & SOCIETY 38.4, pp. 1395–1405. Gajos, Krzysztof Z and Lena Mamykina (2022). “Do people engage cognitively with AI? Impact of AI assistance on incidental learning”. In: 27th international conference on intelligent user interfaces, pp. 794–806. Hill-Yardin, Elisa L et al. (2023). “A Chat (GPT) about the future of scientific publishing”. Brain Behav Immun 110, pp. 152–154. Horton, John J (2023). Large language models as simulated economic agents: What can we learn from homo silicus? Tech. rep. National Bureau of Economic Research. Ibrahim, Hazem et al. (2023). “Rethinking Homework in the Age of Artificial Intelligence”. IEEE Intelligent Systems 38.2, pp. 24–27. Jin, Ginger Zhe, Michael Luca, and Daniel Martin (2015). Is no news (perceived as) bad news? An experimental investigation of information disclosure. Tech. rep. National Bureau of Economic Research. Jungherr, Andreas (2023). “Using ChatGPT and Other Large Language Model (LLM) Applications for Academic Paper Assignments”. Korinek, Anton (2023). “Language models and cognitive automation for economic research” Lambrecht, Anja and Catherine Tucker (2019). “Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads”. Management science 65.7, pp. 2966–2981. Malik, Agung Rinaldy et al. (2023). “Exploring Artificial Intelligence in Academic Essay: Higher Education Student’s Perspective”. International Journal of Educational Research Open 5, p. 100296. Mullainathan, Sendhil and Jann Spiess (2017). “Machine learning: an applied econometric approach”. Journal of Economic Perspectives 31.2, pp. 87–106. Obermeyer, Ziad et al. (2019). “Dissecting racial bias in an algorithm used to manage the health of populations”. Science 366.6464, pp. 447–453. Pallathadka, Harikumar et al. (2023). “Applications of artificial intelligence in business management, e-commerce and finance”. Materials Today: Proceedings 80, pp. 2610–2613. Rambachan, Ashesh et al. (2021). “Identifying prediction mistakes in observational data”. Harvard University. Ray, Partha Pratim (2023). “ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope”. Internet of Things and Cyber-Physical Systems. Rolf, Esther et al. (2020). Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. arXiv: 2003.06740 [cs.LG]. Salah, Mohammed, Hussam Al Halbusi, and Fadi Abdelfattah (2023). “May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research”. Computers in Human Behavior: Artificial Humans, p. 100006 Schmohl, Tobias et al. (2020). “How Artificial Intelligence can improve the Academic Writing of Students”. In: Conference Proceedings. The Future of Education 2020. Shahriar, Sakib and Kadhim Hayawi (2023). “Let’s have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations”. arXiv preprint arXiv:2302.13817. Singh, Harjit and Avneet Singh (2023). “ChatGPT: Systematic Review, Applications, and Agenda for Multidisciplinary Research”. Journal of Chinese Economic and Business Studies 21.2, pp. 193– 212. Steyvers, Mark et al. (2022). “Bayesian modeling of human–AI complementarity”. Proceedings of the National Academy of Sciences 119.11, e2111547119. Sundar, S Shyam and Eun-Ju Lee (2022). “Rethinking communication in the era of artificial intelligence”. Human Communication Research 48.3, pp. 379–385. Tejeda, Heliodoro et al. (2022). “AI-assisted decision-making: A cognitive modeling approach to infer latent reliance strategies”. Computational Brain & Behavior 5.4, pp. 491–508. Thorp, H Holden (2023). ChatGPT is fun, but not an author. Wang, Xinru, Chen Liang, and Ming Yin (2023). “The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets”. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, Edith Elkind (Ed.). International Joint Conferences on Artificial Intelligence Organization, pp. 3076– 3084 Yang, Nanyin, Marco Palma, and Andreas Drichoutis (2023). “How Does Humanizing Virtual Assistants Affect the Propensity to Follow Their Advice?” Yang, Nanyin, Marco A Palma, and Andreas C Drichoutis (2023). “How Does Humanizing Virtual Assistants Affect the Propensity to Follow Their Advice?” Zuiderwijk, Anneke, Yu-Che Chen, and Fadi Salem (2021). “Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda”. Government Information Quarterly 38.3, p. 101577. This paper is available on arxiv under CC 4.0 license. Authors: (1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu); (2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Authors: Authors: (1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu); (2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Table of Links Abstract and Introduction Methods Results Discussion References Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals Abstract and Introduction Abstract and Introduction Methods Methods Results Results Discussion Discussion References References Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals References Agrawal, Ajay, Joshua Gans, and Avi Goldfarb (2019). The economics of artificial intelligence: an agenda. University of Chicago Press. Akram, Arslan (2023). “An Empirical Study of AI Generated Text Detection Tools”. arXiv preprint arXiv:2310.01423. Altmäe, Signe, Alberto Sola-Leyva, and Andres Salumets (2023). “Artificial intelligence in scientific writing: a friend or a foe?” Reproductive BioMedicine Online Athey, Susan and Guido W Imbens (2019). “Machine learning methods that economists should know about”. Annual Review of Economics 11, pp. 685–725. Beck, J Thaddeus et al. (2020). “Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center”. JCO clinical cancer informatics 4, pp. 50–59. Björkegren, Daniel, Joshua E. Blumenstock, and Samsun Knight (2022). (Machine) Learning What Policies Value. arXiv: 2206.00727 [econ.GN]. Bom, Hee-Seung Henry (2023). “Exploring the Opportunities and Challenges of ChatGPT in Academic Writing: a Roundtable Discussion”. Nuclear Medicine and Molecular Imaging, pp. 1– 3. Bommasani, Rishi et al. (2021). “On the opportunities and risks of foundation models”. arXiv preprint arXiv:2108.07258. Bringula, Rex (2023). “What do academics have to say about ChatGPT? A text mining analytics on the discussions regarding ChatGPT on research writing”. AI and Ethics, pp. 1–13. Capra, C. Monica, Matthew Gomies, and Shanshan Zhang (2023). “The Sound of Cooperation and Deception in High Stakes Interactions”. Charness, Gary, Brian Jabarian, and John A List (2023). Generation next: Experimentation with ai. Tech. rep. National Bureau of Economic Research. Chien, Chen-Fu et al. (2020). Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies. Cowen, Tyler and Alexander T Tabarrok (2023). “How to learn and teach economics with large language models, including GPT”. Including GPT (March 17, 2023). Daun, Marian and Jennifer Brings (2023). “How ChatGPT will change software engineering education”. In: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, pp. 110–116. Deza, Arturo, Amit Surana, and Miguel P Eckstein (2019). “Assessment of faster r-cnn in manmachine collaborative search”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3185–3194. Dietvorst, Berkeley J, Joseph P Simmons, and Cade Massey (2015). “Algorithm aversion: people erroneously avoid algorithms after seeing them err.” Journal of Experimental Psychology: General 144.1, p. 114. Dranove, David and Ginger Zhe Jin (2010). “Quality disclosure and certification: Theory and practice”. Journal of Economic Literature 48.4, pp. 935–63. Farrell, Max H, Tengyuan Liang, and Sanjog Misra (2020). “Deep learning for individual heterogeneity: An automatic inference framework”. arXiv preprint arXiv:2010.14694. Fitria, Tira Nur (2021). “Grammarly as AI-powered English writing assistant: Students’ alternative for writing English”. Metathesis: Journal of English Language, Literature, and Teaching 5.1, pp. 65–78. Franchi, Matt et al. (2023). “Detecting disparities in police deployments using dashcam data”. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 534–544. Fudenberg, Drew and Annie Liang (2019). “Predicting and understanding initial play”. American Economic Review 109.12, pp. 4112–4141 Fyfe, Paul (2023). “How to cheat on your final paper: Assigning AI for student writing”. AI & SOCIETY 38.4, pp. 1395–1405. Gajos, Krzysztof Z and Lena Mamykina (2022). “Do people engage cognitively with AI? Impact of AI assistance on incidental learning”. In: 27th international conference on intelligent user interfaces, pp. 794–806. Hill-Yardin, Elisa L et al. (2023). “A Chat (GPT) about the future of scientific publishing”. Brain Behav Immun 110, pp. 152–154. Horton, John J (2023). Large language models as simulated economic agents: What can we learn from homo silicus? Tech. rep. National Bureau of Economic Research. Ibrahim, Hazem et al. (2023). “Rethinking Homework in the Age of Artificial Intelligence”. IEEE Intelligent Systems 38.2, pp. 24–27. Jin, Ginger Zhe, Michael Luca, and Daniel Martin (2015). Is no news (perceived as) bad news? An experimental investigation of information disclosure. Tech. rep. National Bureau of Economic Research. Jungherr, Andreas (2023). “Using ChatGPT and Other Large Language Model (LLM) Applications for Academic Paper Assignments”. Korinek, Anton (2023). “Language models and cognitive automation for economic research” Lambrecht, Anja and Catherine Tucker (2019). “Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads”. Management science 65.7, pp. 2966–2981. Malik, Agung Rinaldy et al. (2023). “Exploring Artificial Intelligence in Academic Essay: Higher Education Student’s Perspective”. International Journal of Educational Research Open 5, p. 100296. Mullainathan, Sendhil and Jann Spiess (2017). “Machine learning: an applied econometric approach”. Journal of Economic Perspectives 31.2, pp. 87–106. Obermeyer, Ziad et al. (2019). “Dissecting racial bias in an algorithm used to manage the health of populations”. Science 366.6464, pp. 447–453. Pallathadka, Harikumar et al. (2023). “Applications of artificial intelligence in business management, e-commerce and finance”. Materials Today: Proceedings 80, pp. 2610–2613. Rambachan, Ashesh et al. (2021). “Identifying prediction mistakes in observational data”. Harvard University. Ray, Partha Pratim (2023). “ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope”. Internet of Things and Cyber-Physical Systems. Rolf, Esther et al. (2020). Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. arXiv: 2003.06740 [cs.LG]. Salah, Mohammed, Hussam Al Halbusi, and Fadi Abdelfattah (2023). “May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research”. Computers in Human Behavior: Artificial Humans, p. 100006 Schmohl, Tobias et al. (2020). “How Artificial Intelligence can improve the Academic Writing of Students”. In: Conference Proceedings. The Future of Education 2020. Shahriar, Sakib and Kadhim Hayawi (2023). “Let’s have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations”. arXiv preprint arXiv:2302.13817. Singh, Harjit and Avneet Singh (2023). “ChatGPT: Systematic Review, Applications, and Agenda for Multidisciplinary Research”. Journal of Chinese Economic and Business Studies 21.2, pp. 193– 212. Steyvers, Mark et al. (2022). “Bayesian modeling of human–AI complementarity”. Proceedings of the National Academy of Sciences 119.11, e2111547119. Sundar, S Shyam and Eun-Ju Lee (2022). “Rethinking communication in the era of artificial intelligence”. Human Communication Research 48.3, pp. 379–385. Tejeda, Heliodoro et al. (2022). “AI-assisted decision-making: A cognitive modeling approach to infer latent reliance strategies”. Computational Brain & Behavior 5.4, pp. 491–508. Thorp, H Holden (2023). ChatGPT is fun, but not an author. Wang, Xinru, Chen Liang, and Ming Yin (2023). “The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets”. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, Edith Elkind (Ed.). International Joint Conferences on Artificial Intelligence Organization, pp. 3076– 3084 Yang, Nanyin, Marco Palma, and Andreas Drichoutis (2023). “How Does Humanizing Virtual Assistants Affect the Propensity to Follow Their Advice?” Yang, Nanyin, Marco A Palma, and Andreas C Drichoutis (2023). “How Does Humanizing Virtual Assistants Affect the Propensity to Follow Their Advice?” Zuiderwijk, Anneke, Yu-Che Chen, and Fadi Salem (2021). “Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda”. Government Information Quarterly 38.3, p. 101577. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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AI Use in Manuscript Preparation for Academic Journals: References

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