From Near-Miss to Policy: Leveraging Small-Scale AI Incidents for Early Governance Intervention

Most current AI incident reporting frameworks, as highlighted in Dillon et al. (2024), focus on learning from major failures, while near-misses and low-severity incidents remain underutilized due to their overwhelming volume and perceived insignificance. This research proposes a novel approach: using AI-powered analytics to systematically mine patterns from near-miss and small-scale incident reports in critical infrastructures (see Agarwal & Nene, 2024), constructing a real-time "early warning" governance dashboard. By surfacing trends and recurrent minor anomalies, the framework would inform dynamic updates to regulatory protocols and sector-specific safety standards before risks escalate. This idea builds on the insight that aviation and healthcare have successfully leveraged such data (Dillon et al., 2024), but such a mechanism is largely absent for AI systems. The anticipated impact is a shift from reactive to anticipatory AI governance, reducing systemic risk and enabling agile policy adaptation.

References:

1. Addressing AI Risks in Critical Infrastructure: Formalising the AI Incident Reporting Process. Avinash Agarwal, M. Nene (2024). IEEE International Conference on Electronics, Computing and Communication Technologies.
2. How AI Can Help Learn Lessons from Incident Reporting Systems. Robin Dillon, Peter Madsen, Brian Holland, Danniel Cao (2024). IEEE Aerospace Conference.