Fernanda Sobrino

Adolfo De Unánue, Fernanda Sobrino

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

Elena Villalobos, Adolfo De Unánue T., Fernanda Sobrino et al.

This article presents the results of a data science study conducted at a container terminal, aimed at reducing unproductive container moves through the prediction of service requirements and container dwell times. We develop and evaluate machine learning models that leverage historical operational data to anticipate which containers will require pre-clearance handling services prior to cargo release and to estimate how long they are expected to remain in the terminal. As part of the data preparation process, we implement a classification system for cargo descriptions and perform deduplication of consignee records to improve data consistency and feature quality. These predictive capabilities provide valuable inputs for strategic planning and resource allocation in yard operations. Across multiple temporal validation periods, the proposed models consistently outperform existing rule-based heuristics and random baselines in precision and recall. These results demonstrate the practical value of predictive analytics for improving operational efficiency and supporting data-driven decision-making in container terminal logistics.