Article Synopsis
- Scientists are excited about the potential of AI tools to enhance research productivity and objectivity, aiming to address human weaknesses.
- However, these AI solutions might exploit our cognitive biases, leading to a false sense of understanding that can obscure significant issues in scientific inquiry.
- The use of AI in research could result in a dominance of certain methods and viewpoints, making science less innovative and potentially leading to errors, highlighting the need for responsible knowledge production in the AI era.
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Article 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.
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