By declaring when and where an AI has been used and for what purposes, researchers can demonstrate honesty in their approach. Some organisations – including the World Conference on Research Integrity – are already adding declarations on AI use to their submission forms. Researchers need to honestly assess the costs and benefits of using these tools, otherwise they are in danger of fooling themselves over the value of these outputs and contributing to the hype and competition around the use of Generative AI.
By developing a greater understanding of how these tools work, researchers can apply academic rigour and use them appropriately for each task. Rigorous checking of outputs can help avoid errors in the use of the content they generate. This is line with guidance from the Russell Group about engaging with AI tools in an appropriate, scholarly way.
We are all learning about Generative AI, so sharing specific details of how they have been used – the prompts, the edits, the failures – is in line with open research practices. Being open and transparent about the processes of using AI, communicating learnings and sharing findings, can help the whole field advance.
Researchers who take seriously the concerns of those whose intellectual property may have been compromised by an AI in its training set, and who wish to avoid filling the scientific literature with error or possible plagiarism, are demonstrating care and respect for those conducting and participating in research. Behind the scenes of many AI platforms are workers who are often being paid very small amounts of money to moderate, check and train these models. By engaging with the debate about the ethical and environmental issues behind generative AI, and making moral choices about which organisations to support and which tools to use, researchers can make sure they and their funding is being used positively.
And by taking ownership of the final content produced using generative AI – checking for error, bias, originality, consistency – researchers can show accountability for their use. By ensuring that inputs are handled appropriately, researchers can show they are accountable and working in line with relevant frameworks and guidance of their funders and institutions. And by being prepared to ask for help if they encounter issues – and through funders and institutions being sensitive to the needs and possibilities for error in this space and handling issues sensitively – researchers now can help develop better frameworks for the future.
Generative AI is something we are all likely to be using, so learning about it now and establishing good principles is important. How much do we think about the technology behind search engines, route planners, web browsers and email, for instance, compared with when they first became available? Perhaps this learning process is particularly important for researchers. Those working long hours in high-pressured environments with multiple commitments and pressure to publish and secure funding may be particularly drawn to the time-saving elements of this software.
Take a typical researcher writing a grant application. They understandably turn to Generative AI to help write more persuasive content for their application, feeding in examples of their own writing, refining their research questions, drawing data from previous papers.
From their individual perspective this a perfectly sensible use of a new tool, and early adopters of these techniques might well get a boost. But once these tools become commonplace (which arguably they already are), will this dilute the benefits as AI drives a homogenisation of written language, a reversion to the mean? Or perhaps it produces an AI-arms race amongst researchers, striving to write better prompts to make the applications more likely to succeed, in turn producing AI haves and have-nots, with inequalities based on institutional support, access to training and funds to access the latest AI tools.
Consider the others engaging with this imaginary grant application. If privacy issues can be overcome, might not overworked grant reviewers receiving verbose AI-assisted and AI-generated content use AI to create summaries and digests, and help expand bullet-pointed notes into full reviewer reports? Might not committee members value having AI assistants to help compare applications and even assist in framing clearer questions for applicants (which applicants might anyway be able to predict using AI trained on their grant, profiles of panel members, and the content of the awarding body website)? Are these sensible uses of technology to enhance writing and synthesis skills, or do they begin to erode the human agency and community that is part of the social fabric of research life?
ChatGPT’s Razor
Just as universities and schools are wrestling with the potential for students to use ChatGPT shortcuts in assessments, so funding bodies, universities and others who are asking for content from researchers should consider the impact of generative AI on the tasks they are setting.
Perhaps one use of generative AI can be obtained without even switching on a computer. Instead, generative AI can power a thought experiment.
If Generative AI can produce an answer to a question on a form as well as a human being, what is the value of that question? Is it possible that we don’t actually need to ask the question anymore? Or do we need a different way to assess the underlying goal behind the question?
I propose that this ‘ChatGPT Razor’ could be a useful tool – one that has no environmental cost, breaches of privacy or risk of plagiarism – for identifying and trimming unnecessary bureaucracy. Such a razor might help reduce the workload on researchers, free up reviewers’ time and help those making decisions focus on the key information, ultimately improving research culture, and relieving some of the pressure to use AI tools in the first place.
Tips for using Generative AI like a scientist
Use your record keeping skills
Use the principles of good scientific record keeping to help maintain transparency around the use of Generative AI. Keep track of your interactions with Large Language Models and record:
- What model and version/s you’ve used
- What prompts you have entered, and note how different prompts give different outputs
- Keep a safe, clean copy of any original writing or other content you share with an AI
- If you edit text after it has been generated, keep track of your changes
These approaches will help you show exactly where Generative AI was involved in your work, and give you evidence to declare this usage (in cases where that becomes necessary)
Research the alternatives
Consider which generative AI tool is best suited for your specific approach, rather than just using a generic tool. For example, compared with ChatGPT 3.5:
- ai and Elicit.com are AI tools designed for finding publications and help avoid hallucinations (the generation of plausible but non-existent references)
- Bing Chat Enterprise operates in a more secure environment (inputs are not fed into the training set) and is capable of drawing on up-to-date information directly from the Internet
- The Wolfram Alpha plugin for ChatGPT can help produce higher-confidence data sets, is more transparent in it’s working, and can better handle mathematical questions
- Custom research programs such as AlphaFold might be best for specific research questions
Apply critical thinking
- Fact-check Generative AI content: ensure references are verified, code is tested, and details are consistent with other sources of information
- Look for biases – the first response from a prompt may look great, but is it telling the whole story?
- Be sceptical – if something looks too good to be true, it may well not be true
Share your findings
We’re all getting to grips with these new tools, so if you find something useful, consider sharing your learning with colleagues
