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

Kato Nabirye H.

Faculty of Business, Kampala International University, Uganda

ABSTRACT

Conflict early-warning systems (CEWS) enhanced by big data analytics represent a transformative approach to predicting and preventing violent conflict. This study explores the intersection of ethics, accuracy, and governance in the deployment of such systems, emphasizing their interdependence within complex socio-technical environments. Drawing on existing literature and multi-context evidence, the paper examines how big data sourced from social media, satellite imagery, and transactional records improves predictive capabilities through advanced methodologies such as machine learning, anomaly detection, and probabilistic forecasting. However, these innovations introduce critical challenges, including data bias, privacy violations, lack of transparency, and risks of political manipulation. The study highlights the importance of robust evaluation metrics, data quality assurance, and model interpretability in ensuring predictive reliability. It further analyzes governance frameworks, focusing on accountability mechanisms, stakeholder involvement, and legal compliance necessary for responsible deployment. Empirical evidence reveals mixed predictive performance, underscoring the limitations of current models and the need for methodological rigor and reproducibility. Ultimately, the paper argues that while big data significantly enhances early-warning capacities, its effectiveness depends on embedding ethical safeguards and governance structures that ensure fairness, trust, and accountability. The study contributes to the literature by offering a comprehensive framework that integrates technical performance with ethical and institutional considerations.

Keywords: Conflict Early-Warning Systems (CEWS), Big Data Analytics, Political Violence Prediction, Algorithmic Governance, and Ethical AI. https://doi.org/10.59298/ROJAM/2026/5111000