Machine Learning as Performative Materialist Practice: Thirteen Theses on the Epistemology, Methodology, and Politics of Applied ML
For researchers and practitioners in institutional decision-support ML, this provides a critical theoretical framework to rethink model validation and political choices, but it is primarily philosophical and does not present new empirical results.
The paper challenges epistemological commitments in applied ML (e.g., context-free validation, neutral metrics) by proposing a performative materialist framework with 13 theses, arguing models are temporally situated interventions and validity is measured by real-world effects. It unifies practical prescriptions like temporal cross-validation and satisficing as consequences of this epistemology.
Machine learning practice in institutional decision-support contexts -- government, public policy, public health, criminal justice, resource allocation -- rests on a set of largely unexamined epistemological commitments inherited from classical statistics and computer science: that models represent stable regularities, that validation can be context-free, that performance metrics are politically neutral, and that feature importance reveals system structure. This paper challenges these commitments through a unified framework of performative materialist ML, articulated as thirteen theses. Drawing on Pickering's cybernetic ontology, the performativity literature from economic sociology (Callon, MacKenzie), Simon's bounded rationality, the formalization of performative prediction (Perdomo et al., 2020), and fifteen years of applied ML experience in government and public policy, we argue that: (1) ML models are best understood not as truth-seeking representations but as temporally situated compressions that function as instruments of intervention; (2) the full data product is a complex adaptive system that coevolves with its target and navigates a multi-objective space no single algorithm can optimize; (3) validity is fundamentally performative, measured by effects in the world rather than formal properties of the model; (4) the choices embedded in objective functions, fairness criteria, and resource thresholds are political decisions belonging to stakeholders, not technicians. We show how these theses unify several practical prescriptions -- temporal cross-validation, precision and recall at k, pipeline-aware fairness auditing, satisficing over optimizing -- as consequences of a coherent materialist epistemology rather than isolated best practices