Maritime AI deployment gates should test uncertainty, data quality and human oversight
Research on explainable failure prevention outlines a governance frame for predictive-maintenance and failure-prevention systems, while leaving production validation, universal performance and binding legal duties unresolved.
The proposed maritime architecture is conceptual and modular. It connects data acquisition, anomaly detection, time-series prediction, risk assessment, explanation and actuation in a closed loop. It describes actions being implemented only when decision confidence is high, with lower-confidence outcomes sent through the process again.
That design places uncertainty and data quality near the decision point. The research identifies unseen operating regimes, noisy sensors, missing values and shifting data distributions as conditions that can reduce reliability and hinder decisions. It presents robustness to those conditions as a future research need, not as a completed deployment result.
For governance review, standard accuracy metrics are not enough to describe maintenance readiness. The research recommends examining false alarms, missed detections, detection delay, computational cost and real-time applicability, alongside human-in-the-loop decision support that lets operators and engineers use anomaly scores, forecasts and explanations.