## 3 AI Implementations in Maritime Transport Systems
### AI in Hazardous Material Handling
Integrating AI systems with existing maritime infrastructure can be challenging. Maritime operations often rely on legacy systems that were not designed to integrate with modern AI technologies, leading to compatibility issues. Extensive modifications and upgrades to current infrastructure are often necessary, which can be costly and time-consuming [51; 52]. Successful AI integration also requires consideration of cultural and environmental factors within the maritime industry. Seafarers and other stakeholders must adapt to new technologies, which necessitates training and the fostering of a supportive atmosphere. Resistance to change and lack of familiarity with AI systems can hinder their effective implementation [49; 50; 51; 52]. The effectiveness of automated response systems depends on the quality and time-liness of the data received. In remote maritime environments with limited connectivity, real-time data transmission can be challenging, potentially delaying critical response actions [52]. To address these challenges and enhance the effectiveness of AI in hazardous material handling, several recommendations are proposed. Enhancing sensor technology to improve durability and reliability under maritime conditions is crucial. Standardizing data collection methods and establishing robust data management practices can help maintain data quality and reliability. Investing in advanced data transmission technologies, such as satellite communication systems, can mitigate connectivity issues and enhance real-time data availability. A phased approach to AI integration, starting with pilot projects and gradually scaling up, allows organizations to identify and address potential issues early on, ensuring smoother adoption. Comprehensive training programs for seafarers and other stakeholders are vital to equip them with the necessary skills to operate AI tools and interpret their outputs, fostering a supportive atmosphere for technological integration. In further consideration of this section, it is also necessary to discuss the possible risks arising from the use of AI systems to control dangerous cargo and the potential consequences of the failure of these systems (Figure 3). 1. Data Input Errors  
The data input errors can be defined as the most basal risk that pertains to any inaccuracies while entering data and other data input problems such as non-completed forms or missing pages. These errors can, for instance, cause improper cargo separation and increase the likelihood of adverse reactions from the chemicals or leaks occurring during the movements. It is necessary in such a case to have very strict processes in place for the validation of the data before the AI system begins processing it. The likelihood of these errors occurring can be greatly reduced through the use of standardized and complete procedures to guide the applications of data input processes. 2. Software Malfunctions  
AI software responsible for managing hazardous cargo can also be vulnerable to bugs or glitches, which may disrupt its analysis. Due to such malfunctions, spills and  
Figure 3: Potential risks of AI system failures in the handling of hazardous cargo and preventive measures. other accidents aboard the vessel, such as cargo being handled incorrectly, may escalate. Appropriate measures to reduce these risks to a minimum include routine maintenance, systematic upgrades of the relevant software, and thorough cleansing of the software to allow only the safe ones to remain bundled. It provides assurance of the integrity of the system due to the readiness and operation of the software.