<scp>ChatGPT</scp>and a new academic reality:<scp>Artificial Intelligence‐written</scp>research papers and the ethics of the large language models in scholarly publishing

Lund, Brady; Wang, Ting; Mannuru, Nishith Reddy; Nie, Bing; Shimray, Somipam R.; Wang, Ziang

doi:10.1002/asi.24750

Cited by 386 publications

(228 citation statements)

References 84 publications

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“…The argument usually given for prohibiting a generative AI tool from being listed as an author is that a requirement of morally responsible publishing is that authors must be accountable for what they write, and generative AI tools lack accountability. 1 The publishing industry seems to have reached a consensus that this is a new norm for publishing, which creates a strong presumption in favor of acceptance. While arguments can be made that generative AI possesses some aspects of authorial accountability, such as the capacity to provide an account or explanation of how an article was created, the aspect of accountability that generative AI genuinely lacks is moral responsibility.…”

Section: Llms or Other Generative Ai Tools Should Not Be Listed As Au...mentioning

confidence: 99%

Editors’ Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing

Kaebnick,

Magnus,

Kao

et al. 2023

Hastings Center Report

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Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work.

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Section: Llms or Other Generative Ai Tools Should Not Be Listed As Au...mentioning

confidence: 99%

Editors’ Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing

Kaebnick,

Magnus,

Kao

et al. 2023

Hastings Center Report

View full text Add to dashboard Cite

show abstract

“…Nature even refers to ChatGPT as a threat (Nature, 2023). One of the concerns is the ability of AI to produce automated scholarly manuscripts (Lund et al, 2023), which could find their way into legitimate or predatory journals, as submitted by humans, but treating AI as a ghost. Although it could be impacted negatively by AI, there are also significant benefits from the use of AI in education (Benuyenah, 2023).…”

Section: Ai‐based Authorship: Does It Have Authorship Rights?mentioning

confidence: 99%

Human‐ and AI‐based authorship: Principles and ethics

Silva

Tsigaris

2023

Learned Publishing

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recommendations for authorship are the dominant guidelines that guide who, and under what circumstances, an individual can be an author of an academic paper.• Large language models (LLMs) and AI, like ChatGPT, given their ability and versatility, pose a challenge to the human-based authorship model.• Several journals and publishers have already prohibited the assignment of authorship to AI, LLMs, and even ChatGPT, not recognizing them as valid authors.• We debate this premise, and asked ChatGPT to opine on this issue. ChatGPT considers itself as an invalid author.• We applied the CRediT criteria to AI, finding that it was definitively able to satisfy three out of the 14 criteria, but only in terms of assistance. This was validated by ChatGPT itself.

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“…Similarly, the pReview software package was developed for automatically generating summarization, contribution detection, writing quality analysis, and potential related works of academic papers to support reviewers (Roberts & Fisher, 2020). Another study found that Natural Language Processing models to generate reviews for scientific papers could make the peer review task easier and more effective, but the reviews were not good enough to replace human experts (Yuan et al, 2021 or perhaps even strategies to help authors correct problems identified by reviewers (Lund et al, 2023). Nevertheless, reviewers normally agree to keep the manuscripts assessed confidential; as such, papers under assessment should not be uploaded to LLMs because they may be saved and incorporated into responses to future questions for other users (Hosseini & Horbach, 2023), or may even grant the LLM owner the right to repurpose the content.…”

Section: Reviewing a Submissionmentioning

confidence: 99%

Artificial intelligence to support publishing and peer review: A summary and review

Kousha

Thelwall

2023

Learned Publishing

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Technology is being developed to support the peer review processes of journals, conferences, funders, universities, and national research evaluations. This literature and software summary discusses the partial or complete automation of several publishing‐related tasks: suggesting appropriate journals for an article, providing quality control for submitted papers, finding suitable reviewers for submitted papers or grant proposals, reviewing, and review evaluation. It also discusses attempts to estimate article quality from peer review text and scores as well as from post‐publication scores but not from bibliometric data. The literature and existing examples of working technology show that automation is useful for helping to find reviewers and there is good evidence that it can sometimes help with initial quality control of submitted manuscripts. Much other software supporting publishing and editorial work exists and is being used, but without published academic evaluations of its efficacy. The value of artificial intelligence (AI) to support reviewing has not been clearly demonstrated yet, however. Finally, whilst peer review text and scores can theoretically have value for post‐publication research assessment, it is not yet widely enough available to be a practical evidence source for systematic automation.

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ChatGPTand a new academic reality:Artificial Intelligence‐writtenresearch papers and the ethics of the large language models in scholarly publishing

Cited by 386 publications

References 84 publications

Editors’ Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing

Editors’ Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing

Human‐ and AI‐based authorship: Principles and ethics

Artificial intelligence to support publishing and peer review: A summary and review

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