Messeri, L., Crockett, M.J.
Nature 627, 49–58 (2024).
Abstract
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
Here is my summary:
The article discusses the growing use of AI tools across the scientific research pipeline, including as "Oracles" to summarize literature, "Surrogates" to generate data, "Quants" to analyze complex datasets, and "Arbiters" to evaluate research. These AI visions aim to enhance scientific productivity and objectivity by overcoming human limitations.
However, the article warns that the widespread adoption of these AI tools could lead to the emergence of "scientific monocultures" - a narrowing of the research questions asked and the perspectives represented. This could create "illusions of understanding", where scientists mistakenly believe AI tools are advancing scientific knowledge when they are actually limiting it.
The article describes two types of scientific monocultures:
- Monocultures of knowing - where research questions and methods suited for AI dominate, marginalizing approaches that cannot be easily quantified.
- Monocultures of knowers - where the standpoints and experiences represented in the research are limited to what AI tools can capture.
The article argues that these monocultures make scientific understanding more vulnerable to error, bias, and missed opportunities for innovation. Raising awareness of these epistemic risks is crucial to building more robust systems of knowledge production.
