Artificial intelligence (AI) is increasingly reshaping international arbitration, yet empirical evidence on its procedural, institutional, and normative effects remains limited. This article addresses that gap by presenting a multi-dimensional measurement scheme, referred to as the Algorithmic Accountability Index (AAI), to systematically assess AI-fueled efficiencies, transparency mechanisms, institutional readiness, and algorithmic instantiation across fifty contemporary arbitral cases and five leading arbitration institutions. Using matched case analysis and composite indexing, the study demonstrates that AI tools reduce average case length by approximately one-third, with the greatest time savings occurring during evidence review and award drafting phases. Accuracy metrics show that document review and clause extraction consistently achieve F1-scores above ninety percent, and these improvements can be achieved with modest computational overhead. Survey data further reveal that 71% of arbitrators report high trust and reduced cognitive load when AI outputs are accompanied by explainable rationales, highlighting the necessity of transparency and interpretability for adoption. Bias diagnostic tests indicate uneven model performance across jurisdictions and languages, signaling the need for fairness audits and standardized disclosure practices. Overall, the findings confirm that AI can enhance efficiency and analytical depth in arbitral workflows without compromising procedural quality, when validated controls and governance mechanisms—operationalized through the AAI—are applied.