Drivers and Barriers of AI Adoption and Use in Scientific Research
- URL: http://arxiv.org/abs/2312.09843v2
- Date: Thu, 22 Feb 2024 18:30:31 GMT
- Title: Drivers and Barriers of AI Adoption and Use in Scientific Research
- Authors: Stefano Bianchini, Moritz M\"uller and Pierre Pelletier
- Abstract summary: We study the integration of AI in scientific research, focusing on the human capital of scientists and the external resources available within their network of collaborators and institutions.
Our results suggest that AI is pioneered by domain scientists with a taste for exploration' and who are embedded in a network rich of computer scientists, experienced AI scientists and early-career researchers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: New technologies have the power to revolutionize science. It has happened in
the past and is happening again with the emergence of new computational tools,
such as artificial intelligence and machine learning. Despite the documented
impact of these technologies, there remains a significant gap in understanding
the process of their adoption within the scientific community. In this paper,
we draw on theories of scientific and technical human capital to study the
integration of AI in scientific research, focusing on the human capital of
scientists and the external resources available within their network of
collaborators and institutions. We validate our hypotheses on a large sample of
publications from OpenAlex, covering all sciences from 1980 to 2020, and
identify a set key drivers and inhibitors of AI adoption and use in science.
Our results suggest that AI is pioneered by domain scientists with a `taste for
exploration' and who are embedded in a network rich of computer scientists,
experienced AI scientists and early-career researchers; they come from
institutions with high citation impact and a relatively strong publication
history on AI. The access to computing resources only matters for a few
scientific disciplines, such as chemistry and medical sciences. Once AI is
integrated into research, most adoption factors continue to influence its
subsequent reuse. Implications for the organization and management of science
in the evolving era of AI-driven discovery are discussed.
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