Guidelines for the assessment of machine learning systems

This presentation introduces the new Guidance Note NI692 developed by Bureau Veritas Marine & Offshore for assessing Machine Learning Systems (accessible here).

This Guidance Note provides recommendations for the transparent and trustworthy development and operation of machine learning systems in marine settings. It emphasizes human oversight and risk-based assessment across the system lifecycle, from data and design to deployment, monitoring, and maintenance.

Key highlights of the Guidance Note include:

• Operational context: description of the ML system's objectives, functions, operational and environmental boundaries (Concept of Operations, Operational Envelope, and Operational Design Domain).
• Functional analysis: identification and description of specific functions and sub-functions relying on the ML system.
• Human oversight: defining system automation and corresponding human supervision, roles and responsibilities, and human-machine interactions.
• Risk management: strategies for risk assessment and mitigation.
• Data governance: ensuring data quality, integrity, and security through data collection and preprocessing.
• Model development: recommendations for testing and validating ML models.
• Operational governance: continuous monitoring and maintenance protocols during operation.

NI692 builds upon the EU AI Act, ISO standards, and the IMO MASS Code, and emphasizes key aspects of ML systems such as transparency, explainability, human oversight, and traceability.