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<p>Against claims of scientific neutrality, this paper interrogates data science as an&#xa0;<i>epistemic regime</i>&#xa0;constituted by three interdependent forces: (1)&#xa0;datafication&#xa0;(reducing lived experience to quantifiable proxies), (2)&#xa0;radical empiricism&#xa0;(privileging correlation over causality), and (3)&#xa0;agnostic science&#xa0;(replacing theory with pattern recognition). We argue this triad marginalizes interpretive depth, conceals normative biases, and legitimizes technocratic governance. Crucially, such a regime is embedded in technological infrastructures (e.g. opaque algorithms) that produce&#xa0;<i>epistemic opacity</i>—obscuring the commercial and political interests shaping knowledge. Drawing on philosophy of science and critical data studies, we reveal how the regime’s rhetorical power stems from illusions of objectivity, enabling unaccountable decision-making in policy, commerce, and social intervention.</p>

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Datafication and radical empiricism: the epistemological foundations of the data-science-big data nexus

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • Marco Briziarelli,
  • Sonia Stefanizzi

摘要

Against claims of scientific neutrality, this paper interrogates data science as an epistemic regime constituted by three interdependent forces: (1) datafication (reducing lived experience to quantifiable proxies), (2) radical empiricism (privileging correlation over causality), and (3) agnostic science (replacing theory with pattern recognition). We argue this triad marginalizes interpretive depth, conceals normative biases, and legitimizes technocratic governance. Crucially, such a regime is embedded in technological infrastructures (e.g. opaque algorithms) that produce epistemic opacity—obscuring the commercial and political interests shaping knowledge. Drawing on philosophy of science and critical data studies, we reveal how the regime’s rhetorical power stems from illusions of objectivity, enabling unaccountable decision-making in policy, commerce, and social intervention.