AI-Assisted Risk Assessment in National Security Translations

Abstract

This paper examines the novel use of artificial intelligence (AI) in risk assessment frameworks for national security applications. Contemporary scholarship and practice indicate a significant deficiency: although AI-driven neural machine translation (NMT) is extensively utilized in U.S. intelligence and national security agencies (e.g., for Open-Source Intelligence [OSINT] and Signals Intelligence [SIGINT]), a standardized risk-aware methodology to assess the security, reliability, and error-propagation risks associated with these translations is conspicuously absent. This research presents a unique Risk-Aware Translation Framework (RATF) that integrates Probabilistic Risk Assessment (PRA) methodologies with AI-enhanced translation systems. The framework incorporates human-in-the-loop auditing and AI governance systems, providing a systematic enhancement to the discipline. Empirical evidence indicates that RATF markedly enhances the identification of semantic distortions, biases, and misclassification hazards. The ramifications are significant for U.S. national security entities, indicating the potential for AI-assisted translation to be utilized judiciously, with stringent control, to improve trust, precision, and robustness in multilingual intelligence endeavors.