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  1. What should scientists do about (harmful) interactive effects?Caterina Marchionni & Marion Godman - 2022 - European Journal for Philosophy of Science 12 (4):1-16.
    The phenomenon of interactive human kinds, namely kinds of people that undergo change in reaction to being studied or theorised about, matters not only for the reliability of scientific claims, but also for its wider, sometimes harmful effects at the group or societal level, such as contributing to negative stigmas or reinforcing existing inequalities. This paper focuses on the latter aspect of interactivity and argues that scientists studying interactive human kinds are responsible for foreseeing harmful effects of their research and (...)
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  • On the very idea of pursuitworthiness.Jamie Shaw - 2022 - Studies in History and Philosophy of Science Part A 91 (C):103-112.
    Recent philosophical literature has turned its attention towards assessments of how to judge scientific proposals as worthy of further inquiry. Previous work, as well as papers contained within this special issue, propose criteria for pursuitworthiness (Achinstein, 1993; Whitt, 1992; DiMarco & Khalifa, 2019; Laudan, 1977; Shan, 2020; Šešelja et al., 2012). The purpose of this paper is to assess the grounds on which pursuitworthiness demands can be legitimately made. To do this, I propose a challenge to the possibility of even (...)
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  • Who ought to look towards the horizon? A qualitative study on the collective social responsibility of scientific research.Vincenzo Politi - 2024 - European Journal for Philosophy of Science 14 (2):1-26.
    There is a growing concern for the proper role of science within democratic societies, which has led to the development of new science policies for the implementation of social responsibility in research. Although the very expression ‘social responsibility of science’ may be interpreted in different ways, many of these emerging policy frameworks define it, at least in part, as a form of anticipative reflection about the potential impacts of research in society. What remains a rather under-discussed issue is the definition (...)
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  • AI under great uncertainty: implications and decision strategies for public policy.Maria Nordström - 2022 - AI and Society 37 (4):1703-1714.
    Decisions where there is not enough information for a well-informed decision due to unidentified consequences, options, or undetermined demarcation of the decision problem are called decisions under great uncertainty. This paper argues that public policy decisions on _how_ and _if_ to implement decision-making processes based on machine learning and AI for public use are such decisions. Decisions on public policy on AI are uncertain due to three features specific to the current landscape of AI, namely (i) the vagueness of the (...)
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