Shobita Parthasarathy warns that software designed to summarize, translate and write like humans might exacerbate distrust in science.
Shobita Parthasarathy says that LLMs could help to advance research, but their use should be regulated.could change how science is done — but not necessarily for the better, says Shobita Parthasarathy, a specialist in the governance of emerging technologies at the University of Michigan in Ann Arbor., Parthasarathy and other researchers try to anticipate societal impacts of emerging artificial-intelligence technologies called large language models .
But the algorithmic summaries could make errors, include outdated information or remove nuance and uncertainty, without users appreciating this. If anyone can use LLMs to make complex research comprehensible, but they risk getting a simplified, idealized view of science that’s at odds with the messy reality, that could threaten professionalism and authority. It might also exacerbate problems of public trust in science.
LLMs could be useful in digital humanities, for instance: to summarize what a historical text says about a particular topic. But these models’ processes are opaque, and they don’t provide sources alongside their outputs, so researchers will need to think carefully about how they’re going to use them. I’ve seen some proposed usages in sociology and been surprised by how credulous some scholars have been.
Publishers might strike licensing deals, of course, making their text available to large firms for inclusion in their corpora. But I think it is more likely that they will try to retain control. If so, I suspect that scientists, increasingly frustrated about their knowledge monopolies, will contest this. There is some potential for LLMs based on open-access papers and abstracts of paywalled papers. But it might be hard to get a large enough volume of up-to-date scientific text in this way.
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