Continuous Validation Loops: Integrating Human Oversight into AI-Based Incident Detection and Reporting

Recent work (Azeem et al., 2025) shows that AI algorithms can overestimate incident frequency due to data quality or contextual misinterpretation, leading to governance errors. Similarly, Singh et al. (2024) demonstrate the power of hybrid AI-human approaches for annotating complex incident reports. This research proposes a generalizable, sector-agnostic pipeline where AI-generated incident detections are routinely validated by human experts and frontline personnel, with feedback loops to retrain models and refine detection criteria. The system would also log instances of false positives/negatives and systematically analyze reasons for discrepancies. This approach challenges the assumption that automation should replace human judgment in reporting pipelines, instead positioning human oversight as a continuous, integral part of incident governance. The anticipated impact is a dramatic increase in reporting accuracy, improved trust in AI-powered safety tools, and a strong foundation for adaptive, evidence-based policy updates.

References:

1. I-SIRch: AI-powered concept annotation tool for equitable extraction and analysis of safety insights from maternity investigations. Mohit Kumar Singh, Georgina Cosma, Patrick Waterson, Jonathan Back, G. T. Jun (2024). International Journal of Population Data Science.
2. Internal Validation of an AI-Assisted Algorithm for PC-06.0 (Unexpected Complications in Term Newborns) Measures. Nabila Azeem, Hailey Cox, Dani Zoorob, David Harper, James Van Hook, Hind Moussa (2025). North American Proceedings in Gynecology & Obstetrics.