AI in government and the fair allocation of resources
- Article
- Public Policy
Before AI, resource decisions in public administration ran through slow, hierarchical approval chains. They were prone to human bias, used incomplete data, and could not process large volumes of information, so allocation was inconsistent and some communities were left out. AI and data-driven tools promise faster, more transparent, and more participatory decisions, but they carry their own risks: biased algorithms, data privacy, and opaque models.
This post, published by UP Data Science Society on April 24, 2025 with content and design by Niccolo De Castro and Ivy Ringcunada, summarizes a 2024 paper by K. Perera, N. Fernando, T. Jayasinghe, I. Saleem, and A. Batan (the reference slide also lists D. Kumari) on modern governance and equitable resource allocation.
Approach
The paper is a review organized around three areas.
AI-driven optimization of resource allocation covers predictive analytics (forecasting demand for medical supplies, predicting population growth for infrastructure, anticipating enrollment), budget optimization and dynamic allocation (reallocating funds in real time during a crisis or to underperforming schools), and addressing systemic bias (regular data audits, transparency in algorithm design, and fairness metrics, because a model trained on historical data inherits historical inequities).
Enhancing transparency and accountability covers open data and participatory governance (dashboards, civic engagement platforms, feedback loops) and automated auditing and fraud detection (spotting duplicate beneficiaries in welfare, irregular billing in healthcare, bidding patterns and conflicts of interest in procurement).
Proper policy building covers capacity building and workforce development, inclusive policy frameworks, and collaborative governance models such as public-private partnerships and multi-stakeholder forums.
Findings
The authors find that predictive analytics works best in healthcare, urban planning, disaster management, education, and social welfare, and that its main contribution is moving resources to where need is forecast rather than where it was last year. AI auditing improves compliance and catches fraud that manual review misses. The limiting factor is not the models but the surroundings: governments need clear ethical guidelines, explainable AI so decisions can be questioned, regular impact assessments with affected communities, and investment in digital infrastructure and literacy. Without that, the benefits concentrate in cities and skip rural and underserved areas, which widens the gap the tools were meant to close.
Action steps
The paper recommends well-trained public workers, interdisciplinary teams, technical training, digital literacy campaigns, and regulatory frameworks that keep AI systems accountable. It also asks for follow-up research once these systems are deployed, to measure whether they actually improved outcomes.
Source: Perera, K., Fernando, N., Jayasinghe, T., Saleem, I., and Kumari, D. (2024). Modern Governance and Ensuring Equitable Resource Allocation in Public Administration through Artificial Intelligence and Data-Driven Insights. ResearchGate