Predicting coups in real time: A 4IR-based Early Warning Framework for the African Union.

The article focuses on a Fourth Industrial Revolution (4IR)-based early warning framework employing a Random Forest machine learning classifier to predict coups in real time within African Union (AU) member states. It integrates multi-dimensional socio-political, economic, social, security, and regional indicators—sourced from datasets like the World Bank and GDELT—to generate dynamic risk assessments that improve upon traditional lagging and subjective methods. The case study of Guinea's 2021 coup illustrates how the model could have provided actionable early warnings months in advance by analyzing converging risk factors such as constitutional crises, economic grievances, ethnic marginalization, and military dissatisfaction. While demonstrating high predictive accuracy, the framework's effectiveness depends on data quality, comprehensive coverage, and ethical human oversight to mitigate bias and contextualize findings. The study underscores the potential of 4IR technologies to enhance the AU's conflict prevention capabilities in alignment with its Agenda 2063 goals.