Explainable PIML (XAI) that outputs uncertainty bounds rather than simple alerts to satisfy classification societies.
Read Time: 6 min read | Published: 2025-04-10
The maritime and offshore industry is heavily regulated. Classification societies and insurers are skeptical of 'Black Box' AI algorithms that cannot explain their decision-making process, preventing fleet-wide adoption of autonomous systems.
We utilize Explainable Physics-Informed Machine Learning (XAI). Unlike standard black-box models, our architecture outputs not just a prediction, but also the uncertainty bounds (e.g., 95% confidence intervals) and the physical parameters driving the decision. This transparency aligns with engineering verification standards.
Uncertainty Quantification (UQ) is not just a feature; it is a regulatory requirement for the future. Black-box models will likely fail upcoming certification reviews for autonomous functions.
Mandate Uncertainty Quantification in all future ML deliverables. This creates a future-proof audit trail that satisfies classification societies and facilitates insurance approval.
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