AI-enabled Diplomacy Dashboards for Conflict Prevention Governance

Thesis Abstract

The contemporary security landscape is increasingly driven by rapid data generation and complex interdependencies, rendering traditional diplomacy approaches insufficient for proactive conflict prevention. This study addresses the gap between real-time data fusion and policy-oriented decision-making by developing and evaluating AI-enabled diplomacy dashboards designed to support governance mechanisms that mitigate escalation and sustain peace. The aim is to design, implement, and validate a dashboard platform that integrates multi-source data streams, including satellite imagery, social media signals, economic indicators, and diplomatic incident reports, and to assess its effectiveness in informing preventive diplomacy decisions. Specific objectives include (1) identifying salient indicators predictive of conflict onset within diverse regional contexts, (2) constructing an interoperable data architecture and an AI-driven analytics layer that generates actionable intelligence for policymakers, (3) evaluating the dashboard’s impact on decision speed, consensus-building, and policy coherence among regional organizations, and (4) delineating governance protocols and ethical safeguards for AI-assisted diplomacy. The study adopts a mixed-methods design anchored in realist evaluation and design science research principles. The population comprises policymakers, diplomats, and analysts from three regional organizations (the Africa Union, the Organisation of American States, and the Association of Southeast Asian Nations) and complemented by 200 semi-structured interviews with practitioners and 25 focus groups consisting of 6–8 participants each. Data collection instruments include system usage logs, expert interviews, policy documents, and a standardized survey capturing perceived usefulness, ease of use, trust, and perceived legitimacy of AI-derived insights. Validity and reliability are ensured through triangulation, pilot testing of instruments (n=30), inter-coder reliability checks for qualitative data (Cohen’s kappa ? 0.70), and test-retest procedures for survey items. Data analysis employs a combination of (i) machine learning techniques for indicator selection and predictive modeling, including random forests, gradient boosting, and SHAP value analysis to explain feature contributions; (ii) time-series econometric methods (VAR/VECM) to examine dynamic relationships among indicators and conflict risk; (iii) thematic analysis for interview and document data; and (iv) multi-criteria decision analysis (MCDA) to assess governance trade-offs under different policy scenarios. The conceptual framework integrates deterrence theory, liberal institutionalism, and information governance theory to hypothesize that AI-enabled dashboards enhance signal-to-noise ratios, accelerate deliberation, and improve policy alignment among regional actors when accompanied by transparent provenance, auditable models, and norm-compliant usage. Expected findings include (a) identification of a parsimonious set of high-salience predictors of conflict onset with regional specificity, (b) evidence that dashboards reduce decision-cycle times by 25–40% in simulated exercises, (c) higher perceived legitimacy and trust in AI-generated insights when manifested through explainable AI components and clinician-style interpretability, and (d) identifiable governance gaps related to data sovereignty, accountability, and bias that necessitate formal governance protocols. The study contributes to knowledge by operationalizing an integrative AI-diplomacy platform that translates complex data ecosystems into policy-relevant intelligence, extending theory on technology-enabled governance in international relations, and providing a replicable methodology for evaluating ICT-driven peacebuilding tools in multi-actor environments. The final conclusion posits that AI-enabled diplomacy dashboards can materially improve preventive diplomacy outcomes if designed with rigorous transparency, stakeholder co-creation, and robust ethical safeguards, and recommendations emphasize (1) scalable data governance frameworks, (2) standardized training and capacity-building for practitioners, (3) iterative experimentation with dashboard interfaces to maximize usability and trust, and (4) cross-regional pilot programs to validate transferability and generalizability across diverse geopolitical contexts.