Synergy between Artificial Intelligence and Good Governance: Deciphering Predictive Dynamics for Optimal Public Performance in Morocco

Abstract

This paper explores the synergistic impact of machine learning and good governance principles on improving the performance of the Moroccan public sector. By mobilizing multidimensional data (audit reports, satisfaction surveys, administrative data) and advanced algorithms (Random Forests, SVM), the study assesses how the pillars of governance - transparency, accountability, leadership, innovation, ethics and sustainable development - influence institutional effectiveness, service quality and user satisfaction. The results reveal that Random Forests significantly outperform SVM in predictive accuracy, thanks to their ability to model non-linear and complex relationships. Transparency and accountability emerge as the key determinants of performance, acting directly on institutional credibility and civic engagement. The other pillars, while relevant, operate mainly via indirect mechanisms or systemic synergies, underlining the need for an integrated approach. Visualizations (correlation matrices, interaction maps) confirm the interdependence of variables and the centrality of the two priority pillars. On a theoretical level, this research validates the contribution of artificial intelligence to deciphering governance dynamics, enriching existing conceptual models. In practical terms, it argues for greater investment in the digitization of decision-making processes, training in data analysis tools and the strengthening of accountability systems. Future prospects include the exploration of emerging AI models (Deep learning), longitudinal analysis of reforms and cross-national comparative studies. This work contributes to an empirical understanding of the levers of effective public governance, aligned with the challenges of socio-economic development in Morocco.