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" Big Data Subjectivity "
Affsprung, Daniel
Dobson, James E
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Latin Dissertation
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English
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| Record Number
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1114495
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| Doc. No
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TLpq2408531144
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| Main Entry
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Affsprung, Daniel
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Dobson, James E
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| Title & Author
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Big Data Subjectivity\ Affsprung, DanielDobson, James E
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| College
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Dartmouth College
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| Date
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2020
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2020
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M.A.L.S.
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| Page No
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153
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| Abstract
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Big data is much discussed in business, government, and healthcare, but the ascendance of the data-driven approach has consequences beyond these areas, detectable in both discourse and cultural practices such as self-quantification. The questions explored in this work, “Can the data speak for itself?” and “Can the data speak for us?” are sparked by discourse which positions data or numbers as a communicator or speaker. The conceptual metaphor evinced by these enunciations (e.g. “The numbers speak for themselves”, “What does the data tell you?”) is articulated in this work and critically examined as a supporting element of big data’s claims to objectivity. That objectivity, relying as it does on the denial of human subjectivity, intention, and interpretation, becomes especially problematic in cases where the data being examined is generated by human action. Such cases employ a kind of knowledge production Antoinette Rouvroy calls data behaviorism, which crucially alters the way subjects are formed by rendering individual motivations and narratives secondary to predictive quantitative models. This work examines the data behaviorist change in subjectivation together with critical analysis of quantified self practices and Foucauldian understandings of cultural neoliberalism, and studies the relationships between these and the 4P healthcare paradigm.
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Algorithm
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Big data
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Cybernetics
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Foucault
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Self-quantification
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Self-tracking
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