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  1. Framing the effects of machine learning on science.Victo J. Silva, Maria Beatriz M. Bonacelli & Carlos A. Pacheco - forthcoming - AI and Society:1-17.
    Studies investigating the relationship between artificial intelligence and science tend to adopt a partial view. There is no broad and holistic view that synthesizes the channels through which this interaction occurs. Our goal is to systematically map the influence of the latest AI techniques on science. We draw on the work of Nathan Rosenberg to develop a taxonomy of the effects of technology on science. The proposed framework comprises four categories of technology effects on science: intellectual, economic, experimental and instrumental. (...)
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  • Narratives of epistemic agency in citizen science classification projects: ideals of science and roles of citizens.Marisa Ponti, Dick Kasperowski & Anna Jia Gander - forthcoming - AI and Society:1-18.
    Citizen science projects have started to utilize Machine Learning to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the results of 12 citizen science projects that used machine learning to optimize classification tasks. We examined the distribution of tasks between (...)
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  • Expert views about missing AI narratives: is there an AI story crisis?Jennifer Chubb, Darren Reed & Peter Cowling - forthcoming - AI and Society.
    Stories are an important indicator of our vision of the future. In the case of artificial intelligence, dominant stories are polarized between notions of threat and myopic solutionism. The central storytellers—big tech, popular media, and authors of science fiction—represent particular demographics and motivations. Many stories, and storytellers, are missing. This paper details the accounts of missing AI narratives by leading scholars from a range of disciplines interested in AI Futures. Participants focused on the gaps between dominant narratives and the untold (...)
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