Machine Learning as Performative Materialist Practice: Thirteen Theses on the Epistemology, Methodology, and Politics of Applied ML

Adolfo De Unánue () and Fernanda Sobrino ()
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Adolfo De Unánue: School of Government and Public Transformation, Tecnológico de Monterrey
Fernanda Sobrino: School of Government and Public Transformation, Tecnológico de Monterrey

No 34, Working Paper Series of the School of Government and Public Transformation from School of Governement and Public Transformation

Abstract: This work proposes thirteen theses for rethinking machine learning as a situated, performative, and temporal practice. It argues that models do not represent stable systems, but rather intervene in them, coevolving with the data, institutions, and decisions they help produce. From this perspective, their value should be evaluated based on their concrete effects, their multi-objective trade-offs, and their capacity to improve public action under real material, ethical, and organizational constraints.

Keywords: Machine learning; automated learning; performative prediction; data products; complex adaptive systems; public policy; model evaluation; multi-objective trade-offs; fairness; algorithmic governance; temporality (search for similar items in EconPapers)
JEL-codes: C45 C53 C63 D81 H83 O33 (search for similar items in EconPapers)
Pages: 7 pages
Date: 2026-05
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