Harnessing AI Diplomacy: Automated Conflict Prediction for International Security

Thesis Abstract

This study examines how artificial intelligence-driven diplomatic tools can enhance predictive accuracy and timeliness in identifying and mitigating interstate conflict, addressing gaps in traditional forecasting that rely on static indicators and human-analyst bottlenecks. The problem centers on the limited agility of conventional early-warning systems to incorporate real-time data streams, adversarial misinformation, and nuanced diplomatic signals, potentially delaying crisis prevention and escalation avoidance. The aim is to develop and validate an integrated AI diplomacy framework capable of automated conflict prediction and proactive policy recommendation that supports international security decision-making. Specific objectives include (1) designing an AI-driven pipeline that fuses socio-political event data, open-source intelligence, economic indicators, and diplomatic communications; (2) evaluating the predictive performance of machine learning models against established baselines using multi-criteria metrics; (3) identifying feature sets and signal patterns most strongly associated with regime stability and interstate aggression; (4) assessing ethical, legal, and governance implications of automated diplomacy tools; and (5) deriving policy-relevant recommendations for integration into existing security architectures. The methodology adopts a mixed-methods research design combining quantitative predictive modeling with qualitative theory refinement. The population comprises publicly available international conflict event datasets and diplomatic communication corpora from major powers and regional actors over a twenty-year period (2004–2023). A stratified sampling approach yields a training set of 1.2 million event records and a held-out test set of 300,000 records, supplemented by 120 expert interviews with foreign policy analysts, diplomats, and security researchers to contextualize model outputs. Data collection instruments include an AI-enabled data fusion pipeline, structured coding schemes for event categorization, and semi-structured interview guides. Instrument validity is ensured through content validation by IR scholars and pilot testing with a subset of events, while reliability is established via inter-coder agreement for qualitative components and cross-validation for quantitative models. Analytical methods comprise supervised machine learning algorithms (random forests, gradient boosting, LSTM-based time-series models) and deep learning approaches (transformer-based encoders) to forecast conflict onset, escalation, and mediation opportunities. Model performance is evaluated against baseline statistical models (logistic regression, ARIMA) using metrics such as precision, recall, F1-score, ROC-AUC, and calibration plots, with additional emphasis on lead time and false alarm rates. Feature engineering focuses on geopolitical variables (alliances, trade dependence, leadership change), information dynamics (media sentiment, misinformation risk scores), economic conditions (sanctions,?? flows), and diplomatic signals (summit attendance, negotiation stances). The theoretical backbone draws on Realist and Liberalist paradigms to interpret signal relevance and on the Technology Acceptance Model to examine adoption barriers within security agencies. A conceptual model is developed to illustrate the interaction between AI-derived risk scores, diplomatic recommendations, and human-in-the-loop oversight. Expected findings anticipate that AI-enabled diplomacy will improve early warning timeliness by 20–35% and reduce misclassification costs by 15–25% relative to traditional indicators, with particular strength in predicting rapid escalation cases following leadership changes and sanctions shifts. The study aims to reveal reliable feature subsets—such as combined economic vulnerability indices with joint statement sentiment trajectories—that robustly forecast conflict without excessive false positives. It is also expected to identify governance thresholds to prevent overreliance on automated outputs and to illuminate ethical considerations in bias mitigation, transparency, and accountability. Contribution to knowledge includes (i) an empirically validated, auditable AI diplomacy framework that integrates multi-source data for automated conflict prediction, (ii) a transferable methodological blueprint for security organizations seeking to operationalize AI tools within decision-making processes, and (iii) theoretical refinements to Liberalist and Realist explanations of signal interpretation in the age of autonomous diplomacy. The study concludes that AI-enabled predictive diplomacy can augment human judgment when embedded within clearly defined governance, risk-management, and human-in-the-loop protocols. Recommendations emphasize establishing standardized data governance, model transparency requirements, periodic calibration with domain experts, and pilot deployments within selected regional security forums to iteratively refine predictive performance and policy relevance.