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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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  • Knowledge-augmented face perception: Prospects for the Bayesian brain-framework to align AI and human vision.Martin Maier, Florian Blume, Pia Bideau, Olaf Hellwich & Rasha Abdel Rahman - 2022 - Consciousness and Cognition 101:103301.
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  • Decentring the discoverer: how AI helps us rethink scientific discovery.Elinor Clark & Donal Khosrowi - 2022 - Synthese 200 (6):1-26.
    This paper investigates how intuitions about scientific discovery using artificial intelligence can be used to improve our understanding of scientific discovery more generally. Traditional accounts of discovery have been agent-centred: they place emphasis on identifying a specific agent who is responsible for conducting all, or at least the important part, of a discovery process. We argue that these accounts experience difficulties capturing scientific discovery involving AI and that similar issues arise for human discovery. We propose an alternative, collective-centred view as (...)
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  • Algorithmic augmentation of democracy: considering whether technology can enhance the concepts of democracy and the rule of law through four hypotheticals.Paul Burgess - 2022 - AI and Society 37 (1):97-112.
    The potential use, relevance, and application of AI and other technologies in the democratic process may be obvious to some. However, technological innovation and, even, its consideration may face an intuitive push-back in the form of algorithm aversion (Dietvorst et al. J Exp Psychol 144(1):114–126, 2015). In this paper, I confront this intuition and suggest that a more ‘extreme’ form of technological change in the democratic process does not necessarily result in a worse outcome in terms of the fundamental concepts (...)
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