AI Governance in Intelligent Fire Detection Technologies
Keywords:
machine learning, fire alarm, smouldering fire, importance of fire, safe AI, malfunctioning, false alarms, current emergency system, explainability, human control, smart monitoring.Abstract
Fire detection can increasingly be supported by artificial intelligence through automated-analysis of smoke, flame, thermal, environmental and camera-sensor signals. Deep-learning methods can enhance fire-visual recognition and may hasten detection in settings that could otherwise receive only limited information from conventional detectors. Earlier research showed the viability of video-based fire-and-smoke CNNs and revealed the advantage of utilizing spatial, temporal, and environmental information. But AI-enabled fire detection is a safety-critical application where false negatives and false positives each have substantial implications. Overly late or averted detection could delay emergency response while false alarms can erode confidence, create disruption, or lead to alarm apathy. NIST research on smoke alarms has long considered both fire-response and nuisance characteristics, underscoring the importance of evaluating these competing objectives in parallel.
This research constructs a novel simulation-based AI Governance Framework for Intelligent Fire Detection, joining detection performance, false-alarm mitigation, missed-fire prevention, uncertainty communication, multi-sensor corroboration, human validation, fail-safe escalation, privacy controls, auditability, and model-drift detection. A synthetic set of 2,400 simulated fire detection episodes was split into 4 architectures: Sensor-Only Baseline, Detection-First AI, Explainable AI, and Responsible AI. No actual fire event, building occupant, emergency response record, camera image, or commercial alarm product was employed. Responsible AI attained a simulated detection recall of 97.2% and precision of
96.8%, false-alarm rate of 3.1%, and missed-detection rate of 2.8%. Logistic regression analysis found that visual ambiguity, model drift, network failure, and unsupported single-sensor confidence heightened high-risk alert potential, whereas multi-sensor corroboration, confidence-based escalation, human validation, and failable-action were mitigating.
The research demonstrates that fire alarms demand more than merely optimizing raw detection sensitivity; responsible use involves technically sound detection, needs-based uncertainty quantification, explicit emergency procedures, subject matter expertise, and ongoing assessment.
