FROM SPEECH RECOGNITION TO ALGORITHMIC TRIAGE: HOW POST-9/11 INTELLIGENCE AUTOMATION RECONFIGURED POWER, BIAS, AND ACCOUNTABILITY

Post-9/11 security reforms normalized large-scale automated surveillance by reframing intelligence failure as a problem of data integration. In parallel, advances in artificial intelligence, particularly speech recognition and voice biometrics rendered spoken communication computable at population scale. This paper argues that voice-based AI operates as algorithmic triage: upstream systems that probabilistically filter, rank, and render speech intelligible prior to human judgment. We formalize algorithmic triage as an epistemic infrastructure with identifiable stages and failure modes, and show why voice is a uniquely powerful and dangerous modality, entangling identity, behavior, and cultural difference in a single signal. We further propose a voice-specific sociotechnical audit framework as a workin- progress research agenda. We argue that algorithmic triage erodes conditions of positive peace by normalizing perpetual suspicion and shifting accountability away from contestable human institutions. Addressing these dynamics requires not only ethical critique, but methodological tools for interrogating how voice AI is embedded within security infrastructures.