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  1. Detecting the unknown in a sea of knowns: Health surveillance, knowledge infrastructures, and the quest for classification egress.Francis Lee - 2022 - Science in Context 35 (2):153-172.
    The sociological study of knowledge infrastructures and classification has traditionally focused on the politics and practices of classifying things or people. However, actors’ work to escape dominant infrastructures and pre-established classification systems has received little attention. In response to this, this article argues that it is crucial to analyze, not only the practices and politics of classification, but also actors’ work to escape dominant classification systems. The article has two aims: First, to make a theoretical contribution to the study of (...)
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  • Personalization as a promise: Can Big Data change the practice of insurance?Arthur Charpentier & Laurence Barry - 2020 - Big Data and Society 7 (1).
    The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis (...)
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  • How should we theorize algorithms? Five ideal types in analyzing algorithmic normativities.Lotta Björklund Larsen & Francis Lee - 2019 - Big Data and Society 6 (2).
    The power of algorithms has become a familiar topic in society, media, and the social sciences. It is increasingly common to argue that, for instance, algorithms automate inequality, that they are biased black boxes that reproduce racism, or that they control our money and information. Implicit in many of these discussions is that algorithms are permeated with normativities, and that these normativities shape society. The aim of this editorial is double: First, it contributes to a more nuanced discussion about algorithms (...)
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