## 3 Algorithmic Errors  
Other egregious concerns are those associated with the existence of deficiencies or shortcomings in AI algorithms. Such errors may also lead to a situation where the system deviates from the safety measures and regulations, increasing the risk of breaches. This can be solved through routine checks and revisions of the algorithms to make certain that security is up-to-date. Such failures in AI systems can also be avoided by effective and constant modification of the system algorithms. System Integration Issues  
Even in cases where AI systems are integrated into the ship systems, AI integration problems can also occur. Poor integration of the two systems may result in poor information exchange, and the safety of the cargo operations may not be guaranteed. It is important to test the entire integration so that there is a guarantee that all the systems will exchange information well and there are no problems with the information provided. Cybersecurity Threats  
Given that AI systems are increasingly connected, they are also susceptible to cybersecurity threats. Vulnerabilities in the system could allow unauthorized access or tampering, leading to the manipulation of cargo segregation settings. This could create dangerous conditions on board. To mitigate this risk, implementing strong cybersecurity measures, such as encryption and access controls, is crucial. A robust cybersecurity framework will protect the system from external threats. Human Factors  
Misinterpretation of AI recommendations or improper manual overrides can lead to human errors in cargo handling. These errors might occur due to inadequate training or misunderstandings of the AI system's outputs. To prevent such occurrences, it is necessary to provide thorough training for all personnel and establish clear oversight procedures to ensure proper decision-making. By addressing both the benefits and challenges of AI in hazardous material handling, this study provides a more nuanced and realistic view. This balanced perspective enhances the credibility and depth of the research, offering valuable insights into the practical applications and limitations of AI in maritime operations (Table 3). Several case studies demonstrate the successful implementation of AI in maritime risk management, particularly in hazardous material handling, highlighting its potential to enhance safety and operational efficiency. To address the reviewer's feedback, we will analyze each case study following a systematic methodology, identifying specific barriers and opportunities. Case Study 1: Hapag-Lloyd's Real-Time Hazardous Material Monitoring  
Hapag-Lloyd, a leading global shipping company, has implemented an AI-powered system for the real-time monitoring of hazardous materials on its vessels. The system uses a network of sensors to continuously track the conditions of hazardous cargo, such as temperature and pressure. AI algorithms analyze the sensor data to detect anomalies and potential risks [53]. In one instance, the system identified a significant temperature increase in a container carrying flammable materials. The AI system triggered an alert, and the crew was able to take immediate action to prevent a potential fire. This real-time monitoring capability has enhanced Hapag-Lloyd's ability to manage hazardous materials safely and comply with international regulations [54]. Barriers:  
* Ensuring sensor accuracy and reliability under harsh maritime conditions. * Integration with existing monitoring systems. * Training crew to respond effectively to AI-generated alerts. Opportunities:  
* Enhanced safety through real-time monitoring and early risk detection. * Improved compliance with international safety regulations. * Reduction in incidents involving hazardous materials. Case Study 2: Evergreen Marine's AI-Powered Compliance Monitoring  
Evergreen Marine, a major shipping company, has implemented an AI-powered compliance monitoring system for hazardous materials.