Regulation, Ethics, and Classification Standards for AI in Marine Engineering

Introduction
AI technology is currently outpacing maritime law. The focus is now on “Responsible AI”, ensuring that algorithms are transparent, fair, and accountable. Several regions are actively developing EU AI Act-aligned standards and IMO guidelines for autonomous ships, emphasising that AI must serve the public good and protect environmental safety.

In emerging economies, the regulatory focus is often on “Sovereignty and Compliance.” These nations want to ensure that AI-driven vessels entering their waters meet international classification standards (like DNV or Lloyd’s Register) without compromising local labor laws or security. This course explores how to navigate this fragmented legal landscape, ensuring that AI implementations are both “Trustworthy” and compliant.

The task is to move beyond “Compliance” and into “Ethical Governance.” Managers will learn how to set up “Ethics Committees” to vet new AI projects, ensuring they don’t contain algorithmic bias that could lead to unfair hiring or discriminatory port-arrival scheduling. This course provides the moral and legal “compass”.

Learning Outcomes

1. Apply regulatory and ethical frameworks to ensure the “Responsible Use of AI” in marine systems.
2. Interpret IMO, ISO, and classification society standards for AI-enabled vessels.
3. Implement ethical AI governance that avoids algorithmic bias in safety or operations.
4. Design “Compliance Dashboards” for multi-jurisdictional maritime operations.
5. Evaluate the legal and liability implications of AI-driven engineering decisions.
6. Lead corporate “Ethics Committees” for maritime AI project approvals and oversight

Learning Objectives

1. Interpret classification society standards (e.g., DNV, Lloyd’s) for smart ship certification.
2. Apply ethical AI governance frameworks to ensure transparency and accountability.
3. Assess compliance and assurance models for autonomous and semi-autonomous systems.
4. Differentiate between “Hard Law” (Regulations) and “Soft Law” (Ethical Guidelines).
5. Explain the “Black Box” problem in AI decision-making to non-technical stakeholders.
6. Analyse the role of AI in Environmental, Social, and Governance (ESG) reporting for investors.