GAIA Speaker

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.

Bérénice Le Glouanec holds a master's degree in Language Technology from the University of Gothenburg. She is the AI and data technical expert at Bureau Veritas Marine & Offshore, working within the Digital and Autonomous Ship team in the Rule Development department. She is responsible for drafting rule notes and recommendations, including NI692, a guideline that addresses the entire lifecycle of machine learning systems and ensures their trustworthiness. She also contributes to the International Association of Classification Societies (IACS) on the revision of Recommendation 183 on Ship Data Quality and follows AI standardization activities within AFNOR and CEN-CENELEC.