IMPROVING THE EFFICIENCY OF FINANCIAL RESOURCE ALLOCATION FOR FUNDING CIVIL SOCIETY INSTITUTIONS THROUGH ARTIFICIAL INTELLIGENCE TECHNOLOGIES
DOI:
https://doi.org/10.5281/zenodo.23683332Abstract
This article examines how artificial intelligence can improve the efficiency of financial resource allocation to civil society institutions. Using official data from Uzbekistan’s 2026 state grant competitions, the study assesses funding coverage, reviewer workload, and allocation risks. A human-in-the-loop model encompassing needs mapping, application screening, scoring support, monitoring, and outcome-based learning is proposed. Three practical recommendations focus on data management, governance, and phased implementation.Keywords
civil society institutions, non-governmental non-profit organizations, artificial intelligence, machine learning, resource allocation, allocation efficiency, state grants, grant competition, social order, decision support system, human-in-the-loop, algorithmic fairness, explainability, data governance, needs assessment, outcome monitoring, digital government, public fund, UzbekistanReferences
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