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.