Interpretable Machine Learning framework for prioritizing projects in low-resource municipalities in Ecuador
Keywords:
Territorial governance, algorithmic explainability, neuro-fuzzy systems, equity in public policy, computational decision supportAbstract
Public investment management in fiscally constrained municipalities faces the structural challenge of allocating scarce resources efficiently while preserving territorial equity and decision-making transparency. In Ecuador, this challenge is intensified by pronounced territorial heterogeneity and limitations in the availability and quality of public data. In response, this study proposes and validates a methodological framework that integrates Soft Computing techniques with Explainable Machine Learning to support the prioritization of public investment projects in low-resource municipalities. The proposed approach combines an adaptive neuro-fuzzy inference system capable of modeling non-linear relationships and handling uncertainty inherent to social and territorial data, with explainability techniques based on SHAP values that enable transparent interpretation of each variable’s contribution to the generated recommendations. The framework was applied to a dataset comprising 75 municipalities, using socioeconomic, demographic, and territorial indicators obtained from official and open data sources, and its performance was benchmarked against traditional models and black-box machine learning algorithms. The results show that the proposed hybrid model achieves predictive performance comparable to more complex black-box approaches, with no statistically significant differences, while offering substantial advantages in terms of interpretability, explanatory stability, and perceived fairness. The study concludes that the proposed framework is not intended to automate public decision-making, but rather to enhance informed deliberation by providing explicit and auditable criteria, thereby strengthening accountability, legitimacy, and the overall quality of municipal public investment management in resource-constrained contexts.
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Copyright (c) 2026 Byron Oviedo-Bayas, Blanca Priscila Carpio-Vanegas, Jomaira Lisbeth Gómez-Villa, Lusitania Gutiérrez-Sánchez
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