Conflict Early-Warning Using Big Data: Accuracy, Ethics, and Governance

Kakungulu Samuel J.

Faculty of Education, Kampala International University, Uganda

                                                                                         ABSTRACT
Conflict early-warning systems have evolved significantly with the emergence of big data, artificial intelligence, and machine learning, offering new opportunities to anticipate violent conflict and support timely preventive interventions. This paper examines the use of big data in conflict early-warning, focusing on the accuracy of predictive models, the ethical implications of large-scale data collection, and the governance mechanisms required for responsible deployment. It reviews the conceptual foundations and historical development of conflict early warning systems, highlighting the transition from traditional indicator-based approaches to data-driven predictive models that integrate social media, news reports, geospatial information, and other open-source datasets. The paper further evaluates the analytical techniques employed, including machine learning algorithms such as Random Forests, Logistic Regression, and Support Vector Machines, while assessing their predictive performance, validation frameworks, robustness, and limitations. Particular attention is given to ethical concerns surrounding privacy, informed consent, data rights, algorithmic bias, transparency, explainability, and accountability. The study also explores governance frameworks, institutional arrangements, legal and regulatory mechanisms, stakeholder engagement, and international collaboration necessary to ensure the responsible use of predictive technologies. Although big-data-driven early-warning systems demonstrate considerable potential for improving conflict prediction and supporting preventive diplomacy, their effectiveness remains constrained by data quality, methodological limitations, contextual complexity, and governance challenges. The paper concludes that maximizing the benefits of big data for conflict prevention requires interdisciplinary collaboration, standardized evaluation frameworks, ethical safeguards, transparent governance structures, and sustained investment in capacity building and data infrastructure to enhance both predictive accuracy and public trust.

Keywords: Conflict early-warning, Big data analytics, Machine learning, Predictive governance and Ethical artificial intelligence.

CITE AS: Kakungulu Samuel J. (2026). Conflict Early-Warning Using Big Data: Accuracy, Ethics, and Governance. IDOSR JOURNAL OF ARTS AND MANAGEMENT 11(2):25-36. https:/doi.org/10.59298/IDOSRJAM/2026/112.2536