The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index; 10 of the 54 sources its claims cite were checked for support.
The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts
Open accessAbstract
In this article, we discuss ethical issues related to using and disclosing artificial intelligence (AI) tools, such as ChatGPT and other systems based on large language models (LLMs), to write or edit scholarly manuscripts. Some journals, such as Science , have banned the use of LLMs because of the ethical problems they raise concerning responsible authorship. We argue that this is not a reasonable response to the moral conundrums created by the use of LLMs because bans are unenforceable and would encourage undisclosed use of LLMs. Furthermore, LLMs can be useful in writing, reviewing and editing text, and promote equity in science. Others have argued that LLMs should be mentioned in the acknowledgments since they do not meet all the authorship criteria. We argue that naming LLMs as authors or mentioning them in the acknowledgments are both inappropriate forms of recognition because LLMs do not have free will and therefore cannot be held morally or legally responsible for what they do. Tools in general, and software in particular, are usually cited in-text, followed by being mentioned in the references. We provide suggestions to improve APA Style for referencing ChatGPT to specifically indicate the contributor who used LLMs (because interactions are stored on personal user accounts), the used version and model (because the same version could use different language models and generate dissimilar responses, e.g., ChatGPT May 12 Version GPT3.5 or GPT4), and the time of usage (because LLMs evolve fast and generate dissimilar responses over time). We recommend that researchers who use LLMs: (1) disclose their use in the introduction or methods section to transparently describe details such as used prompts and note which parts of the text are affected, (2) use in-text citations and references (to recognize their used applications and improve findability and indexing), and (3) record and submit their relevant interactions with LLMs as supplementary material or appendices.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper argues that banning LLMs in scholarly manuscript writing is unreasonable because bans are unenforceable and would encourage undisclosed use, while LLMs can support equity in science. It contends LLMs should not be authors or acknowledged, but should be disclosed via free text, in-text citations/references, and supplementary materials, with a modified APA citation format.
Key findings
- The authors oppose journal bans on LLMs for three reasons: bans are unenforceable, they may encourage undisclosed use, and LLMs can help researchers who are not highly proficient in English and thus promote equity in science.
- LLMs should not be named as authors or mentioned in acknowledgments because they lack free will and cannot be held morally or legally responsible, cannot consent, and cannot approve a final manuscript. The paper discusses examples: an O’Connor editorial in *Nurse Education in Practice* listed ChatGPT as second author, with ChatGPT drafting five out of seven paragraphs; a Zhavoronkov *Oncoscience* paper listed ChatGPT as co-author and sought approval from OpenAI CEO Sam Altman, which the authors reject as insufficient.
- The authors recommend disclosing LLM use through (1) free text in the introduction or methods section describing who used LLMs, when, how, prompts, and affected text; (2) in-text citations and references; and (3) recorded interactions as supplementary material or appendices. They suggest a modified APA reference format: “OpenAI (2023). ChatGPT (GPT-4, May 12 Version) [Large language model]. Response to query made by X.Y. Month/Day/Year. https://chat.openai.com/chat”.
- The paper cites Blanco-Gonzalez et al. (2022), who reported that 33.9% of their manuscript comprised ChatGPT-generated text: identical 4.3%, minor changes 13.3%, and related meaning 16.3%. It also notes that ChatGPT May 12 Version can use different models (GPT-3.5 or GPT-4) and generate dissimilar responses.
Methods
- Conceptual/normative ethics analysis; no empirical sample, systematic review, or statistical analysis is reported.
- The argument draws on published journal editorials and policies (e.g., *Science*, *Nature*, *JAMA*, *Accountability in Research*, WAME, COPE, ICMJE, APA) and illustrative cases of LLM authorship/use.
- Main analysis is philosophical and policy-oriented: it examines free will, responsibility, accountability, consent, and credit allocation, then proposes disclosure best practices and a modified citation format.
Limitations
- Stated in the paper: ensuring consistency of disclosures could be challenging; disclosure only in text raises findability/indexing and paywall-access problems; detailed quantification of LLM contribution is unlikely to be provided consistently and may be impossible for non-quantifiable tasks such as conceptualization.
- Stated in the paper: bans are unenforceable partly because detecting LLM-generated text is extremely difficult and detection tools are unreliable.
- Evident from the text: the recommendations are normative and not empirically tested; the examples are illustrative rather than systematic; the paper does not engage the metaphysical free will debate and offers no data on prevalence or effectiveness of disclosure practices.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
28 Own interpretation · 20 Sources not found · 8 Partly supported · 7 No full text of the sources · 5 No basis given · 4 Not checked yet · 1 Supported · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Furthermore, LLMs can be useful in writing, reviewing and editing text, and promote equity in science.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Supportedcites Hosseini and Horbach, 2023 source
The ‘Roles of reviewers in peer review’ section says LLMs can turn notes into well-written reviews and help with editing tasks such as correcting grammar. ‘Roles of editors in peer review’ says they can assist with decision letters. These passages support the quoted claim about writing, reviewing and editing text. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“With respect to fair allocation of credit, not disclosing LLMs, especially those which provide context-specific suggestions and can generate or substantially affect content, violates norms of ethical attribution because it results in giving undue credit to (human) contributors for work which they did not do (Verhoeven et al., 2023).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Partly supportedcites Verhoeven et al., 2023 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Some journals, such as Science, have banned the use of LLMs because of the ethical problems they raise concerning responsible authorship.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Partly supportedcites Thorp, 2023 sourceExperimental — accuracy not yet measured
The policy passage says Science journals prohibit ChatGPT- or other AI-generated text and graphics in papers, and that an AI program cannot be an author. It links those rules to originality and authors’ accountability. But it does not ban all use of LLMs: it expressly exempts AI-generated research datasets. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“While Accountability in Research, JAMA, and Nature decided to adopt or pursue policies that allow using LLMs under conditions that promote transparency, accountability, fair assignment of credit, and honesty, the editors of Science highlighted ethical problems created by LLMs and banned their use:”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Partly supportedcites Hosseini et al., 2023 sourceExperimental — accuracy not yet measured
The Social and epistemic impacts of peer review section says several journals had attempted to develop LLM guidelines, and the Recommendations call for disclosure and human responsibility. Our copy does not say that Accountability in Research, JAMA, and Nature each adopted or pursued policies allowing LLM use under the stated conditions. The retrieved passages have section labels but no page numbers. (LLM, conf=0.84)
- Partly supportedcites Flanagin et al., 2023 sourceExperimental — accuracy not yet measured
In the main text (page unavailable), the paper says JAMA updated its policies to require disclosure of AI-assisted writing and describes Nature’s policy requiring disclosure while barring AI tools from authorship credit. It does not identify Accountability in Research or establish its policy, and it does not explicitly describe honesty as a condition of these policies. (LLM, conf=0.85)
- Not matchedcites Nature, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Third, LLMs can play an important role in helping researchers who are not highly proficient in English (the lingua franca for most top journals) to write and edit their papers, or review others’ manuscripts (Hosseini and Horbach, 2023), which could promote equity in science (Berdejo-Espinola and Amano, 2023).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Partly supportedcites Hosseini and Horbach, 2023 sourceExperimental — accuracy not yet measured
The Roles of editors in peer review section says LLMs could help non-native English speakers turn their views into presentable reviews. The Roles of reviewers in peer review section also describes manuscript editing and mentions using LLMs to write manuscripts, but does not specifically connect those authoring benefits to researchers with limited English proficiency. Page numbers are unavailable in our copy. (LLM, conf=0.82)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Since LLMs are constantly learning (or in the event of plugging them to the internet, receive new data), responses to the same question few days or weeks apart could be different as was shown recently(Hosseini and Horbach, 2023).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
What the sources say
- Partly supportedcites Hosseini and Horbach, 2023 sourceExperimental — accuracy not yet measured
In “Peer review and reproducibility” (page unavailable), the authors report that an identical ChatGPT query three weeks apart produced dissimilar conversations, and they describe LLMs as learning rapidly. The passage does not establish the parenthetical explanation that connecting an LLM to the internet supplies new data and causes responses to change. (LLM, conf=0.85)
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
In the paper
“because LLMs do not have free will and therefore cannot be held morally or legally responsible for what they do.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Not checked — no full text of the sourcecites Mele, 2006 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, bans are unenforceable.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We argue that this is not a reasonable response to the moral conundrums created by the use of LLMs because bans are unenforceable and would encourage undisclosed use of LLMs.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Bans on LLM use may encourage undisclosed use of LLMs.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Some researchers have already embraced this idea by naming LLMs as authors.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not matchedcites O’Connor, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Zhavoronkov, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
- Probably not a substantial claim. 42
- It may state two separate claims as one. 19
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although LLMs can make some glaring mistakes, are susceptible to bias, and may even fabricate facts or citations (Hosseini et al., 2023), these flaws should not be held against them because human researchers might make similar errors.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Partly supportedcites Hosseini et al., 2023 sourceExperimental — accuracy not yet measured
The Recommendations section warns that LLM-assisted reviews may be biased or outright erroneous, and the Roles of reviewers section describes a biased ChatGPT response. Our copy does not establish the more specific claim that LLMs fabricate facts or citations; searches for fabricated or invented references did not find such a passage. The citing authors’ argument that similar human errors excuse these flaws is not attributed to this source. (LLM, conf=0.85)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Accountability and credit are two sides of the same coin, and contributors cannot have one without the other (Hosseini et al., 2022; Resnik, 1997; Smith, 2017).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Partly supportedcites Hosseini et al., 2022 sourceExperimental — accuracy not yet measured
The “Attribution of Credit” and “Responsibility and Accountability” sections link recognition and accountability to identifiable contributions, and say authors should be accountable for their work. They do not establish the stronger claim that every contributor must have credit and accountability together; the paper distinguishes authorship credit from acknowledgement for different contributions. Page numbers are unavailable in our copy. (LLM, conf=0.83)
- Not checked — no full text of the sourcecites Resnik, 1997 source
- Not checked — no full text of the sourcecites Smith, 2017 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Tools used in research are typically disclosed in the body of the text, and in the case of software applications, they are also cited with in-text citations and in the references (Katz et al., 2021).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
What the sources say
- Partly supportedcites Katz et al., 2021 sourceExperimental — accuracy not yet measured
The Software citation essentials section recommends citing software used in research, including software described in the Methods section. The In-text referencing and Software citation examples sections show in-text citations and reference entries for software. The paper offers guidance and examples, however; it does not establish that research tools are typically disclosed or that software is routinely cited this way. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We argue that naming LLMs as authors or mentioning them in the acknowledgments are both inappropriate forms of recognition because LLMs do not have free will and therefore cannot be held morally or legally responsible for what they do.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Furthermore, LLMs can be useful in writing, reviewing and editing text, and promote equity in science.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Not checked — no full text of the sourcecites Berdejo-Espinola and Amano, 2023 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We argue that naming LLMs as authors or mentioning them in the acknowledgments are both inappropriate forms of recognition because LLMs do not have free will and therefore cannot be held morally or legally responsible for what they do.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“One can be held morally and legally responsible for an action only if that action results from one’s free choices (Mele, 2006).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not checked — no full text of the sourcecites Mele, 2006 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“For example, in an editorial titled “Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?” published in the journal of Nurse Education in Practice, ChatGPT is listed as the second author (O’Connor, 2023).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not matchedcites O’Connor, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both challenges (i.e. findability of articles that used LLMs and identifying what part of the text is affected by their use) could be resolved via general norms of software citation that include in-text citations and referencing.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because LLMs cannot be held morally or legally responsible, they also cannot be held accountable for their actions.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, approval from the CEO of OpenAI should not be considered approval from the author, any more than approval by the corresponding author of a paper should count as approval by other (human) authors.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Let’s not forget that LLMs learn fast and change rapidly, making it vital to disclose not only what version was used but also which model, when and by who.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“That said, ensuring the consistency of disclosures could be challenging (similar challenges are faced in software citation, e.g. see Li et al., 2017) and could be addressed through training and education, as well as promoting best practices.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
What the sources say
- Not matchedcites Li et al., 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although being able to explain what they have done and why would take LLMs a step closer to being accountable, it would still fall far short of the degree of accountability we expect from human beings.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While it is possible that this sometimes might not be the case (e.g. a work might be irresponsibly or even maliciously attributed to someone who would have not agreed to be an author if they were alive), generally posthumous authorship is granted for works that individuals contributed to, and would endorse if they were alive.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Even if prominent research institutions and journals were to adopt such measures, these efforts would likely be in vain, since detecting text that has been generated with LLMs is extremely difficult, partly because LLM-generated text can be altered by human beings to mask it.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Use of LLMs, like the use of other type of software tools, should be cited in-text, followed by being mentioned in the references.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Tools in general, and software in particular, are usually cited in-text, followed by being mentioned in the references.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since LLMs are not persons, these social aspects of authorship do not apply to them, but they do apply to human authors, even dead ones.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Authorship, in this sense, is a form of social capital that is awarded, protected, and exchanged in human relationships (Smith, 2017).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not checked — no full text of the sourcecites Smith, 2017 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We believe that crediting LLMs in the acknowledgments section of a manuscript is inappropriate for largely the same reasons that LLMs should not be named as authors, that is, because they lack free will and therefore cannot consent to being acknowledged.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Recognizing LLMs in the acknowledgments section
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We believe that crediting LLMs in the acknowledgments section of a manuscript is inappropriate for largely the same reasons that LLMs should not be named as authors, that is, because they lack free will and therefore cannot consent to being acknowledged.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Recognizing LLMs in the acknowledgments section
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“Ethical principles, including openness, honesty, transparency, efficient use of resources, and fair allocation of credit (Shamoo and Resnik, 2022) demand disclosing the use of LLMs.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Not checked — no full text of the sourcecites Shamoo and Resnik, 2022 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Openness, transparency, and honesty about used methods and tools are paramount to fostering integrity, reproducibility, and rigor in research.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although LLMs can make some glaring mistakes, are susceptible to bias, and may even fabricate facts or citations (Hosseini et al., 2023), these flaws should not be held against them because human researchers might make similar errors.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If AIs develop to the point where there is compelling evidence that they have free will and can be held responsible and accountable and can participate in society like humans, then they could be named as authors on scholarly publications.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“However, this objection is misleading because corporations, government entities, and research centers can own copyrights,3 can be held morally and legally responsible and accountable and can even approve the final version of a manuscript.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not matchedcites ICMJE, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, we believe the ethical acceptability of posthumous authorship is not a convincing objection to our view because posthumous authors were capable of being held accountable and of approving the final version of the paper when they did the work they are credited for.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Today’s LLMs are neither responsible nor accountable because they lack free will (or self-determination).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“While they can manipulate linguistic symbols and digital data quite adeptly, they lack consciousness, self-awareness, a humanlike understanding of language, and values or preferences (Bogost, 2022; Teng, 2020).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not matchedcites Bogost, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Teng, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“For an individual to make a deliberate choice, they must have consciousness, self-awareness, understanding, the ability to reason, information, and values or preferences (see Mele, 2006; O’Connor, 2022).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not checked — no full text of the sourcecites Mele, 2006 source
- Not matchedcites O’Connor, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“APA advises disclosure in the methods section in research articles or in the introduction in literature reviews, essays or response or reaction papers (McAdoo, 2023).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
What the sources say
- Not matchedcites McAdoo, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
In the paper
“Although some companies, including OpenAI, have developed software designed to recognize LLM-generated text (Hu, 2023), these tools are unreliable and are likely to remain unreliable in finding LLM-generated text as computer scientists and researchers find ways of working around them.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
What the sources say
- Not matchedcites Hu, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“In manuscripts behind a paywall, citations are not accessible to all readers, but corresponding references are often open, and thanks to open citations initiatives (e.g. I4OC) will likely become more accessible.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
What the sources say
- Not matchedcites I4OC
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, in-text citations offer the required signposting to indicate what part of the text is affected by LLMs.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“In research, accountability is essential for promoting integrity, reproducibility, rigor, and other important epistemic and moral values (Shamoo and Resnik, 2022).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not checked — no full text of the sourcecites Shamoo and Resnik, 2022 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, as mentioned earlier, recognition is not only about assigning credit but also involves responsibility and accountability, and LLMs cannot be considered responsible or accountable in the way human beings can be.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Recognizing LLMs in the acknowledgments section
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, we believe the ethical acceptability of posthumous authorship is not a convincing objection to our view because posthumous authors were capable of being held accountable and of approving the final version of the paper when they did the work they are credited for.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“In summary, LLMs should not be named as authors because they cannot be held legally and morally responsible for what they do, and authorship implies responsibility (Copyright Review Board, 2022; Shamoo and Resnik, 2022).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
What the sources say
- Not matchedcites Copyright Review Board, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Shamoo and Resnik, 2022 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“because LLMs do not have free will and therefore cannot be held morally or legally responsible for what they do.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These and other forms of punishment play an important role in deterring misconduct in research (Horner and Minifie, 2011), but punishments cannot affect (let alone deter) LLMs in any way, because they do not have interests, values, or feelings.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > LLMs as authors?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This level of detail is unlikely to be provided consistently by all researchers and is perhaps impossible to calculate when LLMs contribute to tasks that are not quantifiable, such as conceptualization.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When LLMs are used to edit and rewrite manuscripts, they are likely to generate or substantially affect content.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text > Best practices for disclosure of using LLMs
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“According to the developers, each of these versions offers different degrees of reasoning, speed, and conciseness, but more importantly, they provide dissimilar responses to the same prompt.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“One might also ask whether LLM use should be disclosed if they are incorporated into existing word processing programs, such as MS Word, which is likely to happen soon (Kelly, 2023).”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text > Best practices for disclosure of using LLMs
What the sources say
- Not matchedcites Kelly, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, since LLMs can have some useful applications in writing and editing text (especially for those conducting research in a language other than their first language), banning them would not support diversity and inclusion in scholarship.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We argue that banning the use of LLMs would be a mistake because a ban would not be enforceable and would encourage undisclosed use of LLMs.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Conclusion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, since LLMs can have some useful applications in writing and editing text (especially for those conducting research in a language other than their first language), banning them would not support diversity and inclusion in scholarship.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Conclusion
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While we think it is not necessary to disclose the use of LLMs if they are only used in ways that do not generate or substantially affect content, we think that this situation will be rare because LLMs can do so much more than correct grammatical or typographical errors.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Disclosing the use of LLMs in the body of the text > Best practices for disclosure of using LLMs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: LLMs can do so much more than correct grammatical or typographical errors.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We argue that banning the use of LLMs would be a mistake because a ban would not be enforceable and would encourage undisclosed use of LLMs.”
§ The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts > Conclusion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper relies on this without support.
Openness, transparency, and honesty about used methods and tools are paramount to fostering integrity, reproducibility, and rigor in research.
“Openness, transparency, and honesty about used methods and tools are paramount to fostering integrity, reproducibility, and rigor in research.”
The paper relies on this without support.
Bans on LLM use may encourage undisclosed use of LLMs.
“Second, bans may encourage undisclosed use of LLMs, which would undermine transparency and integrity in research and discourage training and education in responsible use of LLMs.”
The paper relies on this without support.
Tools in general, and software in particular, are usually cited in-text, followed by being mentioned in the references.
“Tools in general, and software in particular, are usually cited in-text, followed by being mentioned in the references.”
The paper relies on this without support.
Approval is a form of consent and one cannot consent to something without free will.
“However, it did not approve the final version of the manuscript because approval is a form of consent and one cannot consent to something without free will, which ChatGPT and other LLMs do not currently have, despite some sensational claims to the contrary (de Cosmo, 2022).”
The paper relies on this without support.
Researchers do not mention search engines, scholarly indices, or software like SPSS in the acknowledgments section.
“For example, researchers use search engines, such as Google, and scholarly indices, such as PubMed and Web of Science, to search the extant literature and find resources or use software like SPSS to identify relationships and correlations, but none of these tools are mentioned in the acknowledgments section.”
The paper relies on this without support.
As of May 2023, ChatGPT PLUS allows choosing between GPT-3.5 and GPT-4 from the same version.
“As per May 2023, when using ChatGPT PLUS (the paid version), one can choose between two different models (GPT-3.5 and GPT-4) from the same version (ChatGPT May 12 Version) to generate text.”
The paper relies on this without support.
ChatGPT can store previous interactions on individual user accounts.
“Especially in systems like ChatGPT that can generate dissimilar responses to similar prompts, and also store previous interactions on individual user accounts, collecting this information is required to ensure openness and transparency.”
The paper relies on this without support.
A person may not want to be mentioned in the acknowledgments section if they disagree with the conclusions of a study.
“Moreover, a person may not want to be mentioned in the acknowledgments section if they disagree with the conclusions of a study and do not want to be associated with it.”
The paper relies on this without support.
A mention in the acknowledgments section carries moral and legal weight.
“Although a mention in the acknowledgments section of a paper is not as prestigious as an author byline, it still carries some moral and legal weight and should therefore involve consent.”
The paper relies on this without support.
People mentioned in the acknowledgments section may be held morally or legally responsible for the integrity of data they provided.
“If a person mentioned in the acknowledgments section provided data for a study that comes under suspicion of fabrication or falsification, they may be held morally (and perhaps legally) responsible and accountable for the integrity of the data they provided.”
The paper relies on this without support.
LLMs can do so much more than correct grammatical or typographical errors.
“we think that this situation will be rare because LLMs can do so much more than correct grammatical or typographical errors.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Moreover, the need for guidance is urgent because using AI raises novel epistemological and ethical issues related to objectivity, reproducibility, transparency, accountability, responsibility, and trust in science [9, 102].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [102]
In the citing paper
“This became a major issue for the ethical norms of science in the winter of 2022–2023, when some researchers listed ChatGPT as authors on papers [102].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [102]
In the citing paper
“Currently, there seems to be an emerging consensus that falls in between these two extremes position, namely, that AI systems can be used in preparing manuscripts but that their use should be appropriately disclosed and discussed, [4, 102].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [102]
Citations
Cites
ChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437
ChatGPT listed as author on research papers: many scientists disapprove
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00107-z · Cited by 868
Rethinking Informed Consent in Bioethics
Manson, N. C., & O'Neill, O. (2007) · doi:10.1017/cbo9780511814600 · Cited by 800
The role of AI in drug discovery: challenges, opportunities, and strategies
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690
Tools such as ChatGPT threaten transparent science; here are our ground rules for their use
(2023) · Nature · doi:10.1038/d41586-023-00191-1 · Cited by 605
Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?
O’Connor, S., & ChatGPT (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2022.103537 · Cited by 585
Mele, A. R. (2006) · doi:10.1093/0195305043.001.0001 · Cited by 493
Flanagin, A., Bibbins-Domingo, K., Berkwits, M., & Christiansen, S. L. (2023) · JAMA · doi:10.1001/jama.2023.1344 · Cited by 420
Hosseini, M., & Horbach, S. P. J. M. (2023) · Research Integrity and Peer Review · doi:10.1186/s41073-023-00133-5 · Cited by 328 · cited 2×
“Although LLMs can make some glaring mistakes, are susceptible to bias, and may even fabricate facts or citations (Hosseini et al., 2023), these flaws should not be held against them because human researchers might make similar errors.”
BackgroundSelf-citationUsing AI to write scholarly publications
Hosseini, M., Rasmussen, L. M., & Resnik, D. B. (2024) · Accountability in Research · doi:10.1080/08989621.2023.2168535 · Cited by 286 · cited 4×
“Articles and editorials published in journals, including Nature (Nature, 2023), Accountability in Research (Hosseini et al., 2023), JAMA (Flanagin et al.”
InfluentialSelf-citation
Research works printed but not linked: 6
- Copyright Review Board (2022) Re: Second Request for Reconsideration for Refusal to Register A Recent Entrance to Paradise (Correspondence ID 1-3ZPC6C3
- ICML 2023 (n.d) International Conference on Machine Learning - ICML. Available at: https://icml.cc/Conferences/2023/CallForPapers (accessed 12 April 2023)
- International Committee of Medical Journal Editors (2023) Defining the Role of Authors and Contributors. https://www.icmje.org/recommendations/browse/manuscript-preparation/preparing-for-submission.html (accessed 10 April 2023)
- Mennell R, Burr S (2017) Wills and Trusts. St. Paul, MN: West Publishing.
- Miller A, Davis M (2018) Intellectual Property. St. Paul, MN: West Publishing.
- Nurse Education in Practice (2023) Guide for authors. Available at: https://www.elsevier.com/journals/nurse-education-in-practice/1471-5953/guide-for-authors (accessed 1 April 2023)
Cited by
Sorted by citations
An exploratory survey about using ChatGPT in education, healthcare, and research
Hosseini, M., Gao, C. A., Liebovitz, D. M., Carvalho, A. M., Ahmad, F. S., Luo, Y., MacDonald, N., Holmes, K. L., & Kho, A. (2023) · PLOS ONE · doi:10.1371/journal.pone.0292216 · Cited by 238
Self-citationResnik, D. B., & Hosseini, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00493-8 · Cited by 228
“Moreover, the need for guidance is urgent because using AI raises novel epistemo-logical and ethical issues related to objectivity, reproducibility, transparency, accountability, responsibility, and trust in science [9, 102].”
InfluentialSelf-citationScherbakov, D., Hubig, N., Jansari, V., Bakumenko, A., & Lenert, L. A. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf063 · Cited by 154
Mapping the ethics of generative AI: a comprehensive scoping review
Hagendorff, T. (2024) · Minds and Machines · doi:10.1007/s11023-024-09694-w · Cited by 141
How to optimize the systematic review process using AI tools
Fabiano, N., Gupta, A., Bhambra, N., Luu, B., Wong, S., Maaz, M., Fiedorowicz, J. G., Smith, A. L., & Solmi, M. (2024) · JCPP Advances · doi:10.1002/jcv2.12234 · Cited by 130
Metadata
- Authors
- Hosseini, M., Resnik, D. B., & Holmes, K.
- Affiliations
- Northwestern University (US)
- National Institute of Environmental Health Sciences (US)
- Corresponding author
- Hosseini, Mohammad
- Venue
- Research Ethics
- Publisher
- SAGE Publications
- Published
- 2023-06-15
- Volume
- 19
- Issue
- 4
- Pages
- 449-465
- ISSN
- 1747-0161 (ISSN-L)
- 2047-6094
- PMC
- PMC11694804
- OpenAlex
- W4380997384
- Cited by
- 341
- References
- 28
- Citations by year
- 2026: 148 · 2025: 105 · 2024: 68 · 2023: 20
- FWCI
- 98.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Research Ethics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Center for Advancing Translational Sciences (UL1TR001422)
- National Institute of Environmental Health Sciences
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Keywords
- large language models, authorship ethics, AI disclosure, author responsibility, research transparency, equity in science
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-29, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-29 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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