Data Science as Machinic Neoplatonism

McQuillan, Daniel. 2018. Data Science as Machinic Neoplatonism. Philosophy & Technology, 31(2), pp. 253-272. ISSN 2210-5433 [Article]

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Abstract or Description

Data science is not simply a method but an organising idea. Commitment to the new paradigm overrides concerns caused by collateral damage, and only a counterculture can constitute an effective critique. Understanding data science requires an appreciation of what algorithms actually do; in particular, how machine learning learns. The resulting ‘insight through opacity’ drives the observable problems of algorithmic discrimination and the evasion of due process. But attempts to stem the tide have not grasped the nature of data science as both metaphysical and machinic. Data science strongly echoes the neoplatonism that informed the early science of Copernicus and Galileo. It appears to reveal a hidden mathematical order in the world that is superior to our direct experience. The new symmetry of these orderings is more compelling than the actual results. Data science does not only make possible a new way of knowing but acts directly on it; by converting predictions to pre-emptions, it becomes a machinic metaphysics. The people enrolled in this apparatus risk an abstraction of accountability and the production of ‘thoughtlessness’. Susceptibility to data science can be contested through critiques of science, especially standpoint theory, which opposes the ‘view from nowhere’ without abandoning the empirical methods. But a counterculture of data science must be material as well as discursive. Karen Barad’s idea of agential realism can reconfigure data science to produce both non-dualistic philosophy and participatory agency. An example of relevant praxis points to the real possibility of ‘machine learning for the people’.

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Machine learning, Algorithms, Data science, Counterculture, Standpoint theory, Agential realism, Neo-platonism, Big data, Participation Agency

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8 May 2017Accepted
21 August 2017Published Online
June 2018Published

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Date Deposited:

16 Jun 2017 09:16

Last Modified:

14 Apr 2021 09:33

Peer Reviewed:

Yes, this version has been peer-reviewed.


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