Editor’s Note: Today’s post is by Avi Staiman. Avi is the founder and CEO of Academic Language Experts, an author services company dedicated to assisting academic scholars to elevate their research for publication.
One of the biggest challenges for researchers is ensuring that their study is written in a clear and compelling manner that successfully manages to turn their research results into compelling narrative and figures into impactful scientific discoveries. While solid research design and execution serve as the backbone of meaningful research, the form and style of communication also play a critical role in the acceptance, dissemination, and adoption of the study within academia and beyond.
EAL (English as an Additional Language) authors face a particularly uphill climb trying to convey their novel findings in English, their second (and sometimes third or fourth) language. Writing tools and author services help these researchers to improve the clarity of their arguments, free up their time to focus more on research, increase speed to publication, and gain confidence in their work. Most importantly, these tools offer EAL researchers (the vast majority of researchers in the world) a more level playing field whereby research is evaluated by the quality of its content and not the English skills of its author.
ChatGPT as Author
From the moment ChatGPT was released in November, researchers began experimenting with how they could use it to their benefit to help write systematic reviews, complete literature searches, summarize articles, and discuss experimental findings. I was therefore surprised to see that when addressing the use of GPT, a number of major publishers ignored the far-reaching implications and plethora of use cases, instead zeroing in on one particularly obscure issue, namely, ‘ChatGPT as Author’.
This response seems to be a knee jerk reaction to a handful of attempts to list ChatGPT as a contributing author, which many publishers wanted to reject before the trend gained traction. At least some of the cases whereby authors submitted articles authored by ChatGPT seemed to be theoretical experiments to test the limits of authorship and make a point. Science, Elsevier and Nature were quick to react, updating their respective editorial and publishing policies, stating unconditionally that ChatGPT can’t be listed as an author on an academic paper. Nature went as far as describing GPT as a ‘threat to transparent science’. GPT not being granted authorship seems banal enough and didn’t generate much pushback. As it is, not too many researchers clamor to share their authorship credit with their colleagues, not to mention a Chatbot.
However, publishers seemed to be answering a question that few were asking while avoiding other (more?) important use cases. Can or should authors use ChatGPT or other AI tools in the development and writing of their own research writing? Can it be used for conducting a literature review? What about analyzing results? Or maybe for drafting an abstract from an existing article? These are the important questions authors are asking where publishers seem to leave (too much?) room for interpretation.
Drawing a Hard Line in the Sand
Those publishers who addressed this ‘gray area’ differ in regard to the question of whether ChatGPT can be used for assistance in the research process and the level of detail and clarity of their policies.
After proclaiming that nonhumans can’t qualify for authorship, JAMA leaves some wiggle room (albeit hesitantly) for using ChatGPT for writing assistance. The guidelines require authors to describe the nature of involvement in detail.
Authors should report the use of artificial intelligence, language models, machine learning, or similar technologies to create content or assist with writing or editing of manuscripts in the Acknowledgement section or the Methods section if this is part of formal research design or methods.
This should include a description of the content that was created or edited and the name of the language model or tool, version and extension numbers, and manufacturer.
Science takes a much more black and white approach, essentially banning the use of text generated by ChatGPT altogether:
…we are now updating our license and Editorial Policies to specify that text generated by ChatGPT (or any other AI tools) cannot be used in the work, nor can figures, images, or graphics be the products of such tools. And an AI program cannot be an author. A violation of these policies will constitute scientific misconduct no different from altered images or plagiarism of existing works.
One notable exception is ACS, who seem to be taking a proactive approach to defining guidelines on the proper use of AI technologies. An ACS Energy ‘Viewpoint’ piece entertains the possibility that ChatGPT could employ an ‘assisted-driving approach promising to free researchers’ time from the burden of scientific writing and get them back to the science’. An editorial in ACS Nano outlines detailed best practices and policies for using AI tools.
What ACS understands is that GPT can be a good tool for ideation and writing while not being a source of reliable information. However, at least for now, they seem to be an outlier in the publishing landscape. It is also worth noting that COPE put out a position statement clarifying that authors can use the tools so long as they are properly credited and attributed. Making these distinctions and codifying them in policy seems to be a critical step moving forward.
A Precautionary Reaction?
Reading between the lines of some of these statements may reveal the fear of an onslaught of paper mill submissions. Traditional plagiarism checkers haven’t yet caught up to AI detection and even those tools that do some level of detection can be fooled by putting the AI generated text into paraphrasing tools. No publisher wants to be the next Hindawi or IOP and face mass retractions.
On the other hand, publishers would be wise to leave the back door open for authors to use AI tools in order to support their research for two reasons. First, strictly policing the use of these tools would not only be an exercise in futility, but enforcement could quickly become a nightmare. Second, an arms race seems to already be underway to build out software to detect AI writing. Publishers will likely spend ungodly sums of money on these tools, only to be set back by even better models that can outsmart the detectors. Whether that should be our focus is an important question to ponder before diving in headfirst.
Appearances Can Be Deceiving
When we dive a bit deeper, we find examples of publishers hard at work on AI-human collaborative projects that they see as the future of publishing. Springer Nature recently announced their first AI-augmented research highlight and there seems to be much more to come. Cureus, (also a Springer Nature publication) is running a contest calling authors to submit GPT-assisted case reports and JMIR Publications recently wrote a call for papers for a special issue using GPT.
In order to make important decisions on the right response to GPT, we need to revisit a number of fundamental questions about how we understand issues in science including the definition of authorship, what we consider plagiarism and the nature of research writing. If we can come to some consensus on the values that drive science publication forward, then we have a shot at developing meaningful, sophisticated policy.
What is Authorship in Research?
In order to answer this question, we need to get at the heart of how we conceive ‘authorship’ in the research context. We only need to look at the contentious topic of authorship inclusion and order on multi-author papers to understand how quickly the question of authorship becomes entangled.
Authorship as accountability
Nature editor-in-chief Magdalena Skipper argues that “an attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs”. If researchers are coming with their own results, ideas and conceptions of their field and get help with putting it all together, does that make their research no longer novel or are they no longer accountable? Does authorship mean that the listed authors have to write every word, format every reference and insert every comma or that they take responsibility for the end result?
Could authors potentially take responsibility for reviewing and vetting ideas produced by LLMs? Even if GPT generates some of the data or narrative for a study, the human ‘prompt engineers’ would still assume the burden of a) the prompting itself and b) ensuring veracity of information through their own critical review and revisions.
WAME recommendations posit that chatbots cannot meet ICMJE authorship criteria, particularly “final approval of the version to be published” and “agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.” However, this definition requires a bit of honest reflection and introspection. After all, there are many author contributors to articles (such as students) who don’t have final approval of the version to be published and aren’t accountable for “all aspects of the work”.
Authorship as substantial contribution
In addition to accountability for the work produced, COPE guidelines call for “substantial contribution to the work”. In the CREDIT taxonomy of author contribution, there are 14 different ways to make a significant contribution that merit authorship in a paper and I would argue that ChatGPT could make a significant contribution in at least 10 of them. Could authors potentially contribute significantly and be held accountable while also heavily relying on GPT as both an initial source of information and authorial personal assistant?
What Is Plagiarism in Research?
One of the most fundamental principles of scientific authorship is that ideas, words and concepts need to be either original or properly attributed. The word plagiarism has its roots in the Latin plagiarius, “kidnapper”. If texts aren’t original, they constitute plagiarism, a cardinal sin in academic publishing. Therefore, it behooves us to ask: should generating and using AI-assisted text constitute plagiarism?
The answer to that question depends on exactly how ChatGPT works and how plagiarism is defined. Regarding the former, it seems that the risk of unknowingly copying large chunks of texts directly from other sources is unlikely, although small fragments of sentences may indeed be found elsewhere.
More importantly, we need to define plagiarism. If we understand plagiarism to be the use of materials that authors don’t write in their own name, then AI-assisted writing should be considered plagiarism. However, if plagiarism requires taking ideas from others and passing them off as your own, then GPT may not be considered plagiarism as these texts are new and not ‘ripped off’ from someone else. Academic writing inherently includes building a scaffolding of previous work around which the researcher can add novelty.
What is Writing in Research?
If we are going to heavily regulate GPT use, then maybe we should clamp down on the use of scientific ghostwriters who do much of the legwork in the writing process (especially in industry) and rarely receive acknowledgement, forget authorship for their contribution. We also need to consider whether we want to ask authors about other forms of AI writing assistance help they use. Tools such as Grammarly, Writeful, and even Microsoft grammar checker are relied upon heavily by authors. If an author is using GPT for language purposes, why would that need to be declared and other tools not?
Researchers across many fields use software and tools to collect, organize and analyze their data without anyone blinking an eye. What makes our visceral response to writing so very different?
Alternatively, what if authors get their ideas for new research from ChatGPT or have GPT analyze their results but write it up in their own words; might that be ok because the author is technically doing the writing?
I believe that self-respecting researchers won’t use GPT as a primary source the same way they don’t use Wikipedia in that manner. However, they can use it in a myriad of other ways including brainstorming, sentence construction, data crunching, and more. The onus of responsibility for the veracity of information still falls on the researcher but that doesn’t mean we should run to ban because some might use it as a way to cut corners. Adopting a hardline policy, such as the one taken by Science, seems to ignore the vast majority of researchers acting in good faith who simply want to use these tools to further their work.
The Cost of our Fear: Missing the Opportunity to Level the Playing Field for EAL Authors
Publishers who ban GPT are missing a unique opportunity to level the playing field for EAL authors. GPT can be used in many ways to help improve scientific writing in meaningful ways and restricting its use leaves those authors at a significant disadvantage. On the flip side, publishers who seize the opportunity to build on these tools to serve their international communities can engage meaningfully with large audiences that were previously inaccessible or largely unengaged due to their linguistic disadvantage, leading to greater diversity of representation.
Not only that, a hardline approach could actually boomerang and lead to a decrease in author transparency. Here are just some of the considerations worth noting:
- A recent Nature poll found that 80% of authors have already ‘played around’ with GPT. Many of those same authors won’t know what the particular publisher’s policy is and may unknowingly “constitute scientific misconduct no different from altered images or plagiarism of existing works” .Do we really want to criminalize researchers who don’t read the fine print terms and conditions?
- ChatGPT has already been integrated into Bing Chat and will soon be integrated into Microsoft Word. Banning GPT may soon mean banning the use of search and word processors. It is also very hard to define exactly how GPT is used in a particular study as some publishers demand, the same way it is near impossible for authors to detail how they used Google as part of their research. WAME’s guideline to “declare the specific query function used with the chatbot” seems to demand an unrealistic burden of documentation.
- Researchers in many fields are already using a myriad of AI tools such as Elicit, Semantic Scholar, and Scite. Do we need to go back and retract those papers because they used AI tools without proper attribution?
Conclusion: What do we really want our scientists doing?
The proliferation of powerful AI tools pushes us to ask fundamental questions about how we perceive the role of scientists in general and the specific role writing plays in their work. In other words, to what degree should we even care that authors write every word of their research in the first place?
I would argue that, at least in the STEM context, the writing process is a means to an end of conveying important findings in a manner that is clear and coherent. If we can do that faster and cheaper, then maybe we should pause to consider the potential benefits. Would we have held up lifesaving Covid research because the PI got help for an AI tool?
There may be room for an important distinction between HSS and STEM work in this context. I can see justification for stricter policies in cases where the act of writing constitutes an essential part of the research or in ethnographic and qualitative studies where the author’s role impacts the nature of the study. Additional thought must be put into how we legislate the use of AI tools and looked at on a more granular level.
As a basic premise, I suggest that publishers encourage researchers to use all tools at their disposal in order to make their work as accessible and impactful as possible, while continuing to educate researchers on how to find, review, and verify information. At the same time, publishers need to fast track peer review reforms to be ready to contend with even murkier lines between novelty and regurgitation — and fact and fiction.
Special thanks to Roger Schonfeld, David Crotty, Amy Beisel, Adrian Stanley, Sally Wilson and Judah Bernstein for their insightful comments on various drafts.