Explainable AI and Algorithmic Diplomacy: The EL-MDS Framework for Transparent and Equitable Multilateral Decision-Making - Jajere, Ahmed (Global journal of Business and Integral Security, 2026)
The escalating integration of Artificial Intelligence into high-stakes global governance contexts, from refugee allocation to international trade agreements, engenders profound opacity, eroding stakeholder trust and perpetuating epistemic inequalities and data colonialism, particularly impacting the Global South. This study introduces the Explainability Layers and Legibility of Multilateral Decision Systems (EL-MDS) framework, a novel socio-technical architecture designed to enhance transparency, accountability, and legitimacy in AI-driven multilateral decision-making. Employing a convergent parallel mixed-methods design, which integrates quantitative simulation experiments with qualitative analysis of secondary data and expert insights, the research evaluates the impact of EL-MDS on stakeholder trust and policy utility. Results reveal EL-MDS significantly improves perceived legitimacy and practical applicability of AI outputs, demonstrating its capacity to sustain higher trust levels even at elevated transparency. Crucially, ethical AI practices enhance trust only when mediated by robust organizational capabilities. EL-MDS offers a transferable governance blueprint for the ethical integration of AI and operationalizes "algorithmic diplomacy," fostering equitable global AI policy and combating the Digital Cantillon Effect.