The maritime industry is in the throes of a technological revolution, with Artificial Intelligence (AI) steering the ship. From autonomous navigation to smart port systems, AI is transforming how the sector operates, making it more efficient and predictive. With over 90% of world trade carried by sea, the stakes are high, and the potential impact is enormous. The International Chamber of Shipping underscores this, making the adoption of AI in maritime contexts both a strategic and high-impact frontier for global logistics.
The market for maritime AI is expanding rapidly. A recent study by Allied Market Research estimates that the global maritime AI market, valued at $1.06 billion in 2021, is projected to reach $3.73 billion by 2031, growing at a compound annual growth rate (CAGR) of 13.2%. This growth is driven by applications like autonomous vessels, predictive maintenance, intelligent cargo routing, weather forecasting, and vessel traffic management systems. For instance, predictive analytics can reduce vessel downtime by up to 30% and lower maintenance costs by 15%, according to McKinsey & Company.
Ports are also getting smarter. The Port of Rotterdam, Europe’s largest seaport, has implemented an AI-based digital twin to optimise ship arrival times and reduce idle vessel time, cutting CO₂ emissions by approximately 20% per ship call. Similarly, Singapore’s Maritime and Port Authority uses AI-driven traffic systems to manage over 130,000 vessel arrivals annually, showcasing the scalability of these technologies.
However, as AI systems gain influence over high-stakes maritime decisions, critical questions about transparency, fairness, and accountability arise. The European Commission’s AI Act classifies transportation systems involving AI, especially those affecting public and environmental safety, as “high-risk.” This means they require mandatory oversight mechanisms. The maritime sector fits this category squarely, where the consequences of opaque or biased algorithmic decision-making can result in environmental harm, trade disruptions, or human casualties.
A study by Binns et al. found that black-box AI systems, particularly those deployed in safety-critical sectors, can undermine trust and accountability due to the lack of explainability. This concern is compounded in maritime contexts, where jurisdictional overlaps and complex international legal frameworks already challenge incident attribution.
Despite Africa’s pivotal location in global trade, with 38 coastal states and over 90 major seaports, the continent lags significantly in the adoption of maritime AI technologies. The World Bank estimates that African ports handle only 4% of global container traffic, and many operate below 60% of their designed efficiency due to outdated systems and poor digital infrastructure. This gap risks deepening trade inequities and making African ports less attractive to international shipping lines.
The use of foreign-developed AI solutions, trained on non-African datasets, introduces additional risks of bias, misalignment, and unintended consequences. For example, congestion prediction algorithms optimised for ports like Rotterdam or Shanghai may perform poorly in African ports due to infrastructural and logistical differences.
The intersection of underdeveloped regulatory frameworks and increasing interest in AI technologies presents a dual challenge and opportunity. Without ethical and governance frameworks tailored to Africa’s unique maritime environment, the continent risks becoming a passive consumer of opaque AI systems that could entrench inequality, compromise safety, and limit sovereignty. At the same time, this moment offers a strategic opportunity for African maritime regulators to leapfrog traditional digital infrastructure and shape a governance model rooted in transparency, inclusivity, and data sovereignty.
Aligning such efforts with Africa’s Agenda 2063, the IMO’s MASS (Maritime Autonomous Surface Ships) regulatory scoping exercises, and international AI standards such as ISO/IEC 42001:2023 can help ensure equitable participation in the future of maritime logistics.
Key Ethical Concerns in Maritime AI
As AI systems assume greater control over critical maritime operations, their ethical implications are becoming increasingly urgent. Maritime AI applications—from autonomous ship navigation and port scheduling to emissions tracking and cargo routing—are often deployed in high-stakes environments, where errors can result in safety breaches, trade disruptions, or environmental degradation.
Transparency and Explainability
One of the most pressing ethical concerns in maritime AI is the lack of transparency and explainability, particularly in complex systems powered by deep learning algorithms. These “black-box” models often provide outputs without offering understandable rationales, creating challenges in high-risk scenarios such as autonomous navigation or automated collision avoidance. According to Mittelstadt et al., the inability to explain algorithmic decisions undermines user trust, obstructs due process, and limits regulatory oversight. For example, if an AI-based navigation system diverts a vessel and causes an accident, human operators and investigators must be able to trace and comprehend the decision pathway—especially under international maritime liability
