Algorithmic Accountability and Budget Quality in Local Government Financial Management

Abstract

Algorithmic accountability is increasingly important as local governments adopt digital and data-driven tools in budgeting and financial decision-making. This study examines the relationship between algorithmic accountability and budget quality in local government financial management. A quantitative cross-sectional design was employed using survey data from 60 local government officials involved in budgeting, finance, and planning. Data were collected through a structured questionnaire and analyzed using descriptive statistics and simple linear regression. The findings indicate that algorithmic accountability has a positive and statistically significant relationship with budget quality. Greater transparency, explainability, human oversight, data integrity, and auditability were associated with better budget accuracy, allocation relevance, consistency, and responsiveness. The results also show that algorithmic accountability contributes meaningfully to explaining variations in budget quality. These findings suggest that accountable use of algorithmic systems can strengthen budgeting practices and public financial governance by improving the reliability, transparency, and defensibility of local government financial decisions. The study provides practical implications for improving responsible digital budgeting.