I – like every other academic in the world perhaps – am trying to come to terms with the effects of AI on research, teaching, and everything else. This post is about AI use in research.
We recently had a lab meeting about how we are using AI in our research. People expressed lots of amazement about how much you can do with AI coding agents especially. I've personally been having a lot of fun using agents to help me pursue my research agenda (example, this preprint). But students also expressed a lot of disorientation, bewilderment, and skepticism. It's a challenging time to be a trainee scientist! There are many new opportunities for discovery, but the ground is shifting under our feet and it's hard to be sure exactly what's required, what's permissible, and what best practices are.
My north star in all of this is the idea that science is not just for making more PDFs. Our scientific process should be organized around robust and transparent routes to discovery. Used appropriately, AI can lead to faster progress and stronger research. Used inappropriately, it can be a route towards generating papers that no one understands and slowing down communication. Here are a few principles we discussed in our meeting that I'm putting forward as current lab policies.
Policies
No AI-authored text in papers. We will write all the words in our papers. This step maintains human understanding and direction in all of the research we do. It also goes some way to preventing inadvertent errors or fabrications by models, though other checks are critical. To be clear, AI-authored text is fine for some applications (e.g., software documentation or administrative tasks). But let's keep it out of scientific papers – at least, for the moment.
