AI in 911 and Emergency Dispatch

Emergency communications centers handle urgent requests under time pressure. AI may support speech-to-text transcription, translation, call summarization, non-emergency routing, or information retrieval. A mistake can have serious consequences: a transcription may alter a street number, translation may miss a negation, or a summary may omit a symptom or safety concern. Automated tools should therefore fit the center’s protocols, preserve the original audio when permitted, and present uncertainty in a way that does not distract or delay the call-taker. Human dispatchers need authority to override suggestions and access to language assistance. Tests should cover realistic audio conditions and diverse callers, including overlapping speech, accents, noise, and distress. A tool should not determine emergency priority solely from a model score unless a formally approved protocol explicitly supports such use and appropriate oversight is established. Non-emergency routing also requires clear fallback paths when urgency is ambiguous. Centers should monitor errors, escalation, response delay, and disparate performance across communities. Privacy and retention requirements are important because calls can contain sensitive information. Procurement and deployment should include call-takers, dispatchers, emergency managers, privacy and security staff, and public-safety governance. AI may reduce documentation work or help retrieve procedures, but trained personnel remain responsible for assessing the call and coordinating response. Systems should be introduced without delaying urgent assistance. Human fallback should remain available during system outages.