# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 6 Challenges and Limitations
### Ethical and Legal Considerations  
#### 6.2.1 Accountability in Decision-Making  
The integration of Large Language Models (LLMs) into maritime operations introduces complex ethical and legal questions, particularly regarding accountability in decision-making. As these AI systems increasingly influence or even make decisions that affect safety, compliance, and operational efficiency, determining responsibility becomes a critical concern [166].  
One of the primary challenges lies in assigning accountability when AI-assisted decisions lead to adverse outcomes. Traditionally, accountability in maritime operations is clearly delineated among crew members, officers, and management. However, when an AI system contributes to a decision, the lines of responsibility can become blurred [167; 168]. For instance, if an LLM provides erroneous guidance that results in a navigational error or a compliance violation, it raises the question of who is at fault--the AI developers, the maritime organization that implemented the system, or the crew members who relied on its outputs.  
Addressing potential legal implications requires a thorough examination of existing maritime laws and regulations, which may not yet fully encompass the use of AI technologies. The lack of specific legal frameworks governing AI in maritime contexts creates uncertainty about liability and enforcement. Maritime organizations must proactively engage with legal experts to interpret current laws in the context of AI-assisted operations and anticipate how regulations might evolve [161; 169].  
To mitigate these challenges, it is essential to establish clear protocols that define the role of AI systems within decision-making processes. This includes delineating the extent to which human operators can rely on AI outputs and specifying circumstances that require human judgment overrides. Implementing robust oversight mechanisms ensures that human operators remain in control of critical decisions, particularly those that have significant safety implications [170].  
Moreover, transparency in AI operations is vital for accountability. Organizations should adopt Explainable AI (XAI) practices that make the decision-making processes of LLMs interpretable to users. By understanding how an AI system arrives at a recommendation, operators can better assess its reliability and make informed decisions about whether to act on its advice [171].  
Insurance and risk management considerations also play a role. Maritime insurers may require evidence of appropriate risk mitigation strategies when AI systems are used. This could involve demonstrating that the AI has been thoroughly tested, that staff are adequately trained, and that contingency plans are in place should the AI system fail or provide incorrect outputs [161; 172].  
In summary, determining responsibility for AI-assisted decisions necessitates a multifaceted approach that combines legal analysis, policy development, and operational safeguards. By clarifying accountability structures and ensuring that human oversight remains paramount, maritime organizations can navigate the ethical and legal complexities introduced by the adoption of LLMs.