## 3 Algorithmic Errors
### AI-Enhanced Navigation Systems
#### 3.5.4 Critical Assessment of AI in Navigation
Integrating new tools that use AI with the company's and ship's existing IT systems can be technically challenging and require significant resources, infrastructure reconfiguration, and the hiring of skilled professionals who understand both the technical aspects of AI and its application in specific maritime contexts. Lack of expertise can be a major barrier to AI implementation. The team assigned to this must understand the specific needs of maritime transport and create, train, and maintain artificial intelligence models and integrate AI tools into the ship's existing software. Initiatives involving multiple service providers and supply chains require all parties to be open to sharing data and interfaces. Although such collaborative projects often hold significant potential for optimization for all involved, persuading third parties to share their data can be a challenging task. 3. Difficulty in demonstrating business benefits. Another complication standing in the way of implementing AI on ships is the difficulty in estimating and demonstrating the value of a project with AI technologies developing so rapidly. There are difficulties in deciding where and how to integrate AI into existing processes without knowing in which area the potential for benefit is greatest. The lack of this knowledge can be due to a variety of factors, such as insufficient knowledge of AI capabilities, challenges in identifying the best ways to use it, or uncertainty about the potential benefits. 4. Concerns about employment security. The emergence of AI often raises concerns about job displacement due to technology, particularly with robots and autonomous systems. This can create feelings of anxiety and fear among seafarers as well. Moreover, in extreme cases, this anxiety may manifest as deliberate undermining of AI programs or reluctance to adopt this technology. While AI offers substantial benefits in navigation, its role as a primary decision-maker must be critically assessed. One significant challenge is the reliability of AI predictions, which depend on the quality and completeness of the input data. Inaccurate or incomplete data can lead to suboptimal routing decisions, potentially compromising safety. Moreover, the maritime industry has traditionally relied on human expertise and judgment, and a sudden shift to AI-driven decision-making may face resistance from seafarers and other stakeholders [67; 68]. Successful integration of AI-enhanced navigation systems requires not only technological advancements but also a shift in mindset among seafarers and maritime operators. Training and familiarization programs are essential to ensure that seafarers are comfortable with using AI systems and can interpret and act on AI-generated recommendations effectively [69; 70]. Despite AI's significant potential, the challenges associated with its implementation on maritime vessels underscore that human oversight will remain essential for all maritime operations. Human expertise, intuition, and the ability to visually and audibly assess the environment around the ship are currently beyond the capabilities of AI and are crucial for ensuring effective operation and safety.