The Impact Of AI On Scientific Foresight
An exciting new opportunity has come our way! FAR Hub’s PI, Dr Megan Crawford, will be leading a global team of researchers to investigate the effects of AI on human foresight. The team’s expertise span the fields of AI, Sociology, Cognitive Psychology, Computer Science, Scenario Planning, and Business. This opportunity is generously funded by the UKRI Metascience AI Early Career Fellowships.
AI is rapidly transforming how research is conducted, influencing everything from hypothesis generation, to data analysis, to long-term strategic planning. Industries are actively exploring how AI can assist with complex problem-solving and long-term planning in the science and research ecosystem. However, little is known about how these tools shape the way we envision our collective futures.
AI-supported scientific foresight
is an emerging application.
Scientific foresight is a systematic approach to understanding and anticipating plausible, potential, and wildcard futures in science and research to support strategic advancements. Traditionally, foresight relies on human cognition, experience, and reasoning. With the rise of AI, there is growing interest in using AI-generated insights to support or even replace human foresight.
To understand scientific foresight with
AI collaboration is to challenge what it
even means to become “more scientific”.
AI tools could potentially improve qualities of foresight, or they could introduce new risks, or even limit our abilities, such as critical thinking. Without evidence, it is difficult to assess how and whether to prioritize AI into strategic decision-making.
This multi-year investigation will provide crucial insights for various sectors. Research communities will be aided by helping to understand when and how to use AI (e.g. generative) in support of scientific developments. Health sectors could have clearer understandings of whether human-AI collaborations are appropriate in strategic planning for potential pandemics and infectious disease outbreaks. Agricultural industries could be supported in determining how to adopt AI tools for anticipating supply chain disruptions and famine risks.
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