Artificial Intelligence (AI) is rapidly transforming global maritime operations, ushering in a new era of automation, efficiency, and predictive intelligence. From autonomous navigation and smart port systems to real-time emissions tracking and cargo optimisation, AI technologies are becoming integral to how maritime supply chain’s function. According to the International Chamber of Shipping (2022), over 90% of world trade is carried by sea, making the adoption of AI in maritime contexts both a strategic and a high-impact frontier for global logistics.
The market for maritime AI is expanding quickly. A recent study by Allied Market Research(2023) 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%. AI applications include autonomous vessels, predictive maintenance, intelligent cargo routing, weather forecasting, and vessel traffic management systems. For example, predictive analytics can reduce vessel downtime by up to 30% and lower maintenance costs by 15% (McKinsey & Company, 2022). Ports are also becoming increasingly intelligent.
The Port of Rotterdam, Europe’s largest seaport, has implemented an AI-based digital twin of its entire port environment to optimise ship arrival times and reduce idle vessel time, cutting CO₂ emissions by approximately 20% per ship call (Port of Rotterdam Authority, 2023). Similarly, Singapore’s Maritime and Port Authority uses AI-driven traffic systems to manage over 130,000 vessel arrivals annually, demonstrating the scalability of such technologies (MPA Singapore, 2022).
As AI systems gain influence over high-stakes maritime decisions, such as autonomous navigation, customs clearance prioritisation, and emissions scoring, they also raise critical questions about transparency, fairness, and accountability. According to the European Commission’s AI Act (2023), transportation systems involving AI, especially those affecting public and environmental safety, are considered “high-risk” and require mandatory oversight mechanisms. The maritime sector fits squarely within this category, 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. (2018) 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 (2021) 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. Only 5 out of 54 African countries rank above the global median on the UNCTAD Port Liner Shipping Connectivity Index (2023), a key indicator of digital and logistic integration in shipping. Moreover, the AfCFTA Secretariat (2022) highlighted that fewer than 25% of African ports have implemented any form of digital customs processing, making AI integration particularly challenging. This gap risks deepening trade inequities and making African ports less attractive to international shipping lines. Further, 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 (UNCTAD, 2023).
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.
1. Key Ethical Concerns in Maritime AI
As Artificial Intelligence (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. (2016), 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 laws. The EU AI Act (2023) mandates that high-risk AI systems must be “sufficiently transparent” and provide users with clear information about system capabilities, limitations, and decision logic. However, these mandates are often poorly implemented in maritime contexts due to the lack of maritime-specific algorithmic audit standards. As AI adoption increases, this gap could hinder accident investigations, compliance assessments, and claims resolution—key areas for shipping firms and marine insurers.
Fairness and Non-Discrimination
Maritime AI systems increasingly influence how resources are allocated—such as berth slots, inspection prioritization, or even route optimization. When these systems are trained on biased or non-representative data, they risk embedding structural inequalities into maritime logistics. For instance, an AI system trained on historical shipping patterns may favor high-traffic ports, thereby disadvantaging smaller or developing ports in Africa and Southeast Asia. This could reinforce existing trade asymmetries and undermine efforts to improve port competitiveness across emerging economies. A study by Eubanks (2018) in algorithmic governance highlights how biased data inputs can institutionalize discrimination even when decision-makers believe the system to be neutral. Maritime examples could include:
Cargo routing algorithms deprioritizing ports in West Africa due to perceived delays.
Inspection algorithms flagging vessels from lower-income countries at higher rates due to biased historical datasets.
Emissions scoring tools penalizing older ships disproportionately without considering fleet replacement limitations in developing economies.
These risks highlight the need for algorithmic impact assessments (AIAs) that evaluate fairness across geographies, vessel types, and socio-economic factors—especially before deploying AI in transnational logistics networks.
Accountability and Liability
When maritime AI systems malfunction or cause harm, questions of responsibility and liability become legally complex. Traditional maritime law assigns liability to shipowners, operators, or captains. However, with AI-driven systems making autonomous or semi-autonomous decisions, attributing fault becomes murky. For example, in the event of a collision involving an autonomous ship, is the liability borne by the software developer, the shipowner, the AI vendor, or the port authority that cleared the vessel? These challenges are compounded when AI decisions rely on probabilistic models that factor in incomplete data, making it difficult to establish causality. The International Maritime Organization (IMO) acknowledges these issues in its scoping exercise on Maritime Autonomous Surface Ships (MASS), which notes that “current international conventions do not clearly address liability arising from machine-made decisions” (IMO, 2022). Without dedicated regulatory protocols, these liability gaps could deter innovation, stall AI deployment, and expose stakeholders to unpredictable legal risks. Insurance companies are also grappling with this uncertainty. According to a report by Lloyd’s Register (2022), maritime insurers face increasing difficulty in assessing AI-related risks, especially when claims involve AI-generated decisions that cannot be audited or reconstructed.
Algorithmic Risks in African Maritime Contexts
While Artificial Intelligence (AI) promises significant efficiencies in maritime logistics, its deployment in African contexts introduces a unique set of risks. These risks stem not only from technical limitations but also from infrastructural gaps, governance weaknesses, and the continent’s marginal role in AI system design. As African ports seek to modernize, there is growing concern that imported AI technologies may amplify existing inequalities, entrench operational inefficiencies, or generate unintended socio-economic consequences.
Infrastructure Gaps and Digital Readiness Deficit
One of the most pressing challenges is the limited digital infrastructure across many African ports. According to the World Bank (2021), over 60% of Sub-Saharan Africa’s ports lack integrated port community systems (PCS), which are foundational for digitizing and automating port operations. Furthermore, only 30% of African ports have implemented modern cargo tracking and customs data systems (UNCTAD, 2023). This contrasts sharply with ports like Singapore, Rotterdam, or Shanghai, where real-time AI-driven logistics coordination is already standard. These infrastructure deficits severely constrain the functionality of AI systems. For example, AI systems require high-quality, real-time data inputs—such as vessel arrival information, cargo manifests, and port equipment usage metrics—to generate reliable outputs. In environments where data is siloed, manually processed, or inconsistently updated, algorithmic accuracy can deteriorate, leading to flawed predictions and decisions.
Imported AI Systems: Contextual Misalignment
Most AI systems deployed in African maritime settings are developed externally—often in Europe, North America, or East Asia—without accounting for African operational contexts. These systems may be trained on data from hyper-digitized ports, making their predictive models ill-suited to ports that deal with electricity outages, paper-based workflows, or manual cargo inspection. A real-world example is the port of Mombasa, Kenya, which piloted a predictive congestion monitoring tool in 2022. The system, based on algorithms calibrated using European port data, repeatedly underestimated truck turnaround times due to unmodeled variables like informal gate queuing and border-related trade delays. This led to significant planning inefficiencies and shipment delays (Kenya Ports Authority, 2023). Similarly, the port of Lagos, Nigeria has faced frequent disruptions due to AI-based customs processing tools that misclassify goods. These classification errors, often linked to language inconsistencies and poorly labeled datasets, have caused extended clearance delays, affecting exporters and importers alike (Nigerian Shippers’ Council, 2022).
Data Bias and Regulatory Lag
Another critical risk is the lack of localized datasets for training and validating AI models. When African ports rely on foreign datasets, the resulting models often misinterpret local logistics patterns, environmental conditions, and regulatory processes. This raises the risk of algorithmic bias, where certain vessels, operators, or cargo types are unfairly prioritized or penalized. Furthermore, most African maritime authorities are yet to establish AI governance frameworks that can assess, certify, or audit such technologies. The African Union’s Digital Transformation Strategy (2020–2030) acknowledges the lack of AI-specific regulations across the continent and calls for the development of ethical standards, data protection laws, and AI impact assessment protocols. However, implementation remains slow. For example, as of 2023, only four African countries—South Africa, Kenya, Rwanda, and Ghana—had established national AI strategies, and even fewer had maritime-specific AI oversight mechanisms (AUDA-NEPAD, 2023). This regulatory lag increases exposure to unverified AI systems and limits the ability of local actors to contest harmful outcomes.
Amplification of Operational Inefficiencies
Rather than solving existing inefficiencies, poorly implemented AI tools can amplify them. A study by PwC (2022) found that automation technologies introduced without parallel reforms in training, workflows, or governance often worsen bottlenecks. For example:
AI-based crane scheduling systems at a West African port led to idle time increases because operators were unfamiliar with system overrides during power fluctuations.
Automated ship inspection scheduling tools failed to account for limited staff capacity, leading to missed inspections and vessel delays.
Such outcomes erode trust in AI, stall digital transformation, and create feedback loops of inefficiency—especially when there is no local capacity to adjust or re-train the algorithms in response to performance data.
Case Studies – Global Maritime AI in Practice
To better understand the potential and governance challenges of maritime Artificial Intelligence (AI), it is essential to analyze case studies from ports and shipping ecosystems where AI technologies are being actively developed, piloted, and regulated. These case studies offer valuable lessons for African ports and policymakers aiming to adopt AI responsibly while avoiding common pitfalls. This chapter reviews global implementations from Rotterdam, Singapore, Japan and Norway, and emerging African use cases.
Port of Rotterdam, Netherlands – AI for Logistics and Emissions Optimization
The Port of Rotterdam, Europe’s busiest seaport, has emerged as a global leader in maritime AI adoption. Through its collaboration with IBM and Cisco, the port has developed a digital twin that creates a real-time simulation of port operations. This system integrates weather data, vessel movement, traffic flows, and logistics timelines to optimize port calls and berthing schedules. According to the Port of Rotterdam Authority (2023), this AI-driven coordination reduced average vessel waiting times by 20% and cut CO₂ emissions per ship call by approximately 20 metric tons. Moreover, AI is used to predict equipment maintenance needs, reducing crane downtime by up to 30%, thus increasing cargo throughput. The port also applies machine learning models to emissions monitoring and supply chain sustainability. These tools are integrated with the port’s carbon tracking framework, aligning with the EU Green Deal and IMO’s GHG strategy (European Commission, 2023).
Singapore – AI in Maritime Traffic Management and Emissions Scoring
Singapore, consistently ranked as one of the world’s top maritime hubs, has heavily invested in AI-based port optimization and vessel traffic services (VTS). The Maritime and Port Authority of Singapore (MPA) manages over 130,000 vessel arrivals annually and uses AI to forecast traffic congestion, allocate berth slots dynamically, and optimize vessel turnaround times. The Next Generation VTS (NGVT) platform, launched in collaboration with ST Engineering, uses AI to detect anomalies, monitor vessel behavior, and recommend traffic re-routing during peak periods. The platform’s predictive analytics have led to a 15–25% reduction in vessel dwell times and improved traffic flow in the congested Singapore Strait (MPA, 2022). Singapore also uses AI for vessel emissions scoring, assigning real-time ratings to ships based on fuel type, energy efficiency, and operational behavior. This system is linked to incentive schemes—ships with better environmental scores receive priority berthing or reduced port fees (Drewry Maritime Research, 2022). These practices offer replicable models for emissions governance that African ports can adapt as they move toward green port strategies.
Japan and Norway – Autonomous Shipping Pilots and Ethical Testing Protocols
Japan and Norway are at the forefront of autonomous maritime technology development. In 2021, Mitsui O.S.K. Lines (MOL) and NYK Line in Japan successfully conducted trials of autonomous coastal ships, integrating AI with navigation, collision avoidance, and remote diagnostics. The DFFAS (Designing the Future of Full Autonomous Ship) project in Japan, backed by the Nippon Foundation, has established AI ethics review panels to evaluate risk and safety compliance before trials (MOL, 2022). These protocols include simulation audits, human override systems, and fail-safe fallback mechanisms. In Norway, the Yara Birkeland, the world’s first zero-emission autonomous container ship, operates under the supervision of the Norwegian Maritime Authority, which has published ethical risk guidelines for autonomous shipping. These include requirements for explainability, redundancy, and human-in-the-loop oversight, setting an important precedent for ethical AI integration in maritime settings (Norwegian Coastal Administration, 2022).
Africa – Emerging Use Cases and Potential for Scale
While African maritime AI adoption remains limited, several early-stage innovations suggest a growing interest in localized digital solutions:
South Africa’s Transnet National Ports Authority has experimented with drone-assisted AI systems for port perimeter surveillance and berth condition monitoring, improving security response time by 40% (Transnet, 2022).
Kenya is exploring the use of AIS data analytics to monitor IUU (Illegal, Unreported, and Unregulated) fishing, using machine learning to detect anomalous vessel behaviors in its Exclusive Economic Zone (EEZ).
These examples demonstrate the feasibility of maritime AI in Africa, particularly when integrated with contextual data, capacity-building programs, and public-private partnerships. However, scaling such systems requires substantial investment in digital infrastructure, regulatory readiness, and data governance protocols.
International Regulatory Landscape
As Artificial Intelligence (AI) becomes a strategic enabler of modern maritime operations, the need for robust and harmonized governance frameworks has become increasingly urgent. Regulatory efforts are evolving at both international and regional levels, aiming to ensure the ethical, safe, and accountable use of AI in high-risk sectors, including shipping and port logistics. However, the pace and scope of these developments vary significantly across jurisdictions. For African maritime authorities, aligning with global best practices while addressing context-specific realities remains a critical challenge.
IMO’s Preliminary Framework on AI and Maritime Autonomy
The International Maritime Organization (IMO) has begun to explore the implications of AI through its Regulatory Scoping Exercise on Maritime Autonomous Surface Ships (MASS). The final report, released in 2022, acknowledges that existing maritime conventions—such as SOLAS (Safety of Life at Sea), COLREG (Collision Regulations), and STCW (Standards of Training, Certification and Watchkeeping)—do not sufficiently account for machine-based decision-making or algorithmic accountability (IMO, 2022).
The IMO proposes a phased approach to regulation, starting with:
Clarifying definitions of autonomy and AI in maritime contexts,
Assessing legal gaps in existing conventions, and
Piloting new standards through testbeds and stakeholder consultations.
Although the framework is not yet legally binding, it provides a global reference point for national maritime authorities. For Africa, this presents an opportunity to participate proactively in shaping MASS regulations and avoid becoming a passive recipient of externally designed rules.
European Union – The AI Act and High-Risk Systems
The European Union’s Artificial Intelligence Act (2023) is the most comprehensive legal framework for AI to date. It categorizes AI systems into four risk levels—unacceptable, high-risk, limited-risk, and minimal-risk—with transportation and maritime navigation falling under the high-risk category due to safety implications (European Commission, 2023).
Key provisions of the Act relevant to maritime AI include:
Mandatory conformity assessments for AI systems before deployment,
Requirements for human oversight in critical decisions (e.g., autonomous navigation),
Obligations to ensure data quality, transparency, and robustness, and
Creation of national AI supervisory authorities to enforce compliance.
While the Act is EU-specific, it will have extraterritorial impact, affecting any AI developer or shipowner doing business in the European market. African ports and maritime operators engaging in EU-bound shipping will need to understand and potentially align with these standards.
ISO/IEC 42001:2023 – AI Management System Standard
The International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) jointly published ISO/IEC 42001:2023, the first global standard for AI management systems. It provides a structured framework for organizations deploying AI to ensure:
Ethical design and development processes,
Risk-based AI lifecycle management,
Transparency and accountability mechanisms,
Stakeholder engagement and training requirements (ISO/IEC, 2023).
This standard is particularly relevant for maritime logistics providers, port authorities, and shipping firms adopting AI at scale. It complements existing ISO standards such as ISO 9001 (Quality Management) and ISO 27001 (Information Security), which are already common in port operations. For African maritime administrations, adopting ISO/IEC 42001 could serve as a first step toward AI governance readiness.
UNCTAD and World Bank – Maritime Digitalization and Governance Gaps
Reports from UNCTAD and the World Bank emphasize the growing need for AI-specific governance in developing countries. The UNCTAD Review of Maritime Transport (2023) notes that while over 60% of global container traffic flows through ports with AI-enhanced operations, only a fraction of ports in Africa, Latin America, and South Asia have begun formulating AI policy frameworks (UNCTAD, 2023). Similarly, the World Bank’s Digital Ports Toolkit (2021) identifies four critical governance gaps in African ports:
Lack of AI regulations and digital infrastructure standards,
Absence of ethics oversight bodies or review boards,
Fragmented data ownership and interoperability,
Weak institutional capacity to evaluate AI solutions (World Bank, 2021).
These gaps increase exposure to algorithmic risks and reduce leverage in negotiating fair terms with foreign AI vendors and infrastructure investors.
Africa’s Position – Need for Tailored AI Maritime Regulation
While some African countries—such as South Africa, Kenya, Rwanda, and Ghana—have introduced national AI strategies, these documents remain general in scope and rarely address maritime applications. The African Union’s Digital Transformation Strategy (2020–2030) encourages member states to adopt AI frameworks, but uptake has been uneven. To address this, regional bodies such as AUDA-NEPAD, AfCFTA Secretariat, and PMAWCA (Port Management Association of West and Central Africa) could play pivotal roles in:
Coordinating regional maritime AI standards,
Establishing algorithmic oversight units,
Advocating for local data governance regimes, and
Facilitating participation in global regulatory sandbox initiatives.
Without such measures, African ports risk becoming unregulated testing grounds for AI systems developed abroad—exacerbating risks of data exploitation, discrimination, and loss of strategic control.
Policy Recommendations for Ethical Maritime AI in Africa
The deployment of Artificial Intelligence (AI) in Africa’s maritime sector presents both opportunity and risk. On the one hand, AI can enhance operational efficiency, reduce emissions, and improve safety in port and shipping operations. On the other, if unregulated, it can reinforce inequalities, introduce liability gaps, and compromise data sovereignty. To ensure AI is implemented ethically, inclusively, and sustainably, this chapter outlines practical policy recommendations for African governments, port authorities, and regional institutions.
Establish a Pan-African Maritime AI Ethics Council
A centralized body should be created under the auspices of the African Union Commission or AUDA-NEPAD to oversee ethical and regulatory frameworks for maritime AI systems. This Pan-African Maritime AI Ethics Council (PAMAIEC) would:
Set ethical guidelines and governance principles based on international standards (e.g., ISO/IEC 42001, EU AI Act).
Review AI systems for compliance with explainability, fairness, and accountability principles.
Facilitate coordination between national maritime regulators and AI developers.
Promote stakeholder engagement, including input from shipowners, port unions, insurers, and civil society.
This council would serve a similar role to the African Civil Aviation Commission (AFCAC), which harmonizes safety and regulatory standards in aviation.
Develop AI Explainability Protocols for High-Risk Use Cases
Given the high-stakes nature of AI systems in autonomous navigation, collision avoidance, berth allocation, and emissions scoring, African maritime authorities should require explainability protocols. These protocols must include:
Documentation of how AI systems make decisions.
Mandatory human-in-the-loop oversight for critical tasks.
Fail-safe mechanisms that default to manual control during uncertainty.
Periodic performance audits, especially when AI systems replace human judgment.
Explainable AI (XAI) ensures maritime actors can trace system outputs, enabling accountability and facilitating incident investigations—a critical concern under international liability frameworks (Floridi et al., 2020).
Launch Regulatory Sandboxes in Strategic Ports
To balance innovation with governance, African nations should establish AI regulatory sandboxes at key maritime nodes—e.g., Tema (Ghana), Durban (South Africa), and Mombasa (Kenya). These sandboxes allow for controlled, real-world testing of AI systems under regulatory supervision, promoting:
Safe experimentation with port optimization tools and autonomous operations.
Collaborative design of risk thresholds and data-sharing protocols.
Evaluation of algorithmic bias and unintended effects in African settings.
Successful examples of regulatory sandboxes already exist in financial technology across Rwanda and Mauritius (World Bank, 2022). Extending this concept to maritime AI will help build trust and institutional experience.
Mandate Maritime Algorithmic Impact Assessments (AIAs)
Similar to environmental impact assessments (EIAs), any AI system deployed in African ports should undergo a Maritime Algorithmic Impact Assessment (AIA). These assessments would:
Evaluate potential bias in training data or outputs.
Assess effects on worker roles, port competitiveness, and cross-border trade.
Include stakeholder consultations with unions, port communities, and customs authorities.
Establish post-deployment monitoring and feedback mechanisms.
Canada’s federal government and the EU’s AI Act already require such assessments for high-risk AI systems (European Commission, 2023). Africa can adopt a tailored version for its maritime sector.
Implement Real-Time Auditability Standards
To enforce accountability, port authorities and maritime operators should be required to implement real-time auditability standards. These involve:
Logging of AI decision chains and system recommendations.
Secure timestamping and storage of system actions for forensic review.
Regular third-party audits by certified digital inspectors.
Integration with port information systems (e.g., PCS, AIS, VTS).
These standards will facilitate faster investigations, insurance claims, and legal proceedings in the event of AI-related failures, while also deterring irresponsible system design.
Build Human Capacity in AI Risk Governance
African maritime authorities must invest in training programs for AI governance across technical, legal, and operational domains. Target groups include:
Port engineers and IT staff trained in AI system maintenance and risk mitigation.
Regulators and customs officers trained in ethical assessment and compliance monitoring.
Legal professionals versed in AI liability, insurance, and international maritime law.
Public-private partnerships (PPPs) and international donors (e.g., IMO, World Bank, UNCTAD) can support scholarship programs, online courses, and capacity-building initiatives tailored to African maritime contexts.
Building Capacity & Digital Sovereignty
A core pillar of ethical and sustainable maritime AI deployment in Africa is the ability to independently build, manage, and govern digital systems. This requires not only regulatory frameworks but also the technological capacity, human capital, and data sovereignty needed to anchor innovation in local contexts. Without these foundational elements, African ports risk becoming consumers of externally developed, opaque AI solutions—exacerbating dependency, bias, and loss of strategic control.
Strengthening Digital Infrastructure in African Ports
AI systems rely heavily on digital infrastructure—including high-speed internet, sensors, data centers, and automated equipment—to function reliably. However, only 30% of African ports currently operate with integrated Port Community Systems (PCS) and fewer than 20% have real-time cargo tracking capabilities (UNCTAD, 2023).
To close this gap, African governments and regional institutions should:
Prioritize digital port infrastructure grants and investment incentives through national budgets, public-private partnerships, and multilateral donors (e.g., AfDB, World Bank).
Upgrade key components of port information systems (PCS, TOS, VTS) to support AI integration.
Encourage modular infrastructure planning that allows for incremental AI system rollouts as readiness improves.
Ports such as Walvis Bay (Namibia) and Tema (Ghana) are already piloting smart port platforms with sensor integration, creating blueprints for broader replication (World Bank, 2021).
Local Data Ownership and Maritime Data Ecosystems
Data is the fuel of AI. Yet, much of Africa’s maritime data—such as AIS signals, cargo manifests, and port performance metrics—is stored in foreign servers or controlled by third-party logistics providers. This lack of data sovereignty undermines the continent’s ability to train, audit, and govern AI systems locally.
To reclaim control and enable innovation, African nations should:
Mandate that maritime operational data generated within national waters be stored and processed locally.
Establish open-access maritime datasets curated by port authorities, national maritime administrations, or regional bodies like PMAWCA and PMAESA.
Create national data-sharing protocols to enable academic, regulatory, and private-sector innovation while protecting sensitive information.
Models such as South Africa’s National Integrated Maritime Information System (NIMIS) demonstrate how centralized data governance can be used to facilitate both transparency and secure data access.
Foster Public–Private–Academic Innovation Partnerships
To avoid dependency on imported AI solutions, Africa must cultivate local innovation ecosystems that combine academic research, private-sector expertise, and public governance.
Key steps include:
Establishing maritime AI research centers in universities and technical institutes.
Funding startups and SMEs developing context-specific AI tools for navigation, port management, emissions tracking, or customs inspection.
Creating living labs and co-creation hubs within major ports where developers and port staff can collaborate on real-time challenges.
Programs like the Smart Africa Digital Academy (SADA) and AfCFTA Innovation Challenge provide frameworks that can be adapted to the maritime sector.
AfCFTA’s Digital Trade Protocol
The African Continental Free Trade Area (AfCFTA), with its Digital Trade Protocol launched in 2023, offers an opportunity to standardize digital rules across borders. It promotes:
Interoperability of digital systems across African ports,
Harmonization of AI and data governance laws, and
Cross-border collaboration in emerging tech sectors.
In aligning maritime AI development with AfCFTA’s framework, Africa can create a continent-wide digital maritime corridor that enhances intra-African trade, transparency, and competitiveness.
Invest in Skills Development and Digital Literacy
Africa’s digital sovereignty depends on its people. Maritime authorities, port operators, and customs officials must have the skills and knowledge to evaluate, operate, and govern AI systems. Capacity-building programs should include:
Training on data science, machine learning, cybersecurity, and ethical AI for maritime professionals.
Inclusion of AI risk governance in maritime education and certification programs (e.g., STCW upgrades).
Partnerships with international organizations (e.g., IMO, UNCTAD, ITU) to provide technical assistance and certification tracks.
The IMO’s Women in Maritime Program and the World Maritime University’s Maritime Energy Management track are existing platforms that can be expanded to cover AI-specific competencies.
The integration of Artificial Intelligence (AI) into maritime operations marks a transformative chapter in global trade and logistics. From predictive maintenance and emissions tracking to autonomous navigation and smart port optimization, AI systems are increasingly at the core of how modern shipping functions. However, as this technological evolution accelerates, so too does the urgency to embed ethical, legal, and governance principles into their design and deployment—particularly in digitally developing regions like Africa. This article has demonstrated that while AI offers compelling benefits for maritime efficiency and environmental sustainability, it also introduces complex ethical risks. Issues such as algorithmic opacity, biased decision-making, and unclear liability frameworks pose tangible threats to safety, fairness, and legal accountability. In high-stakes environments like shipping lanes, port customs operations, and vessel traffic control systems, these risks cannot be treated as secondary. Rather, they must be integral to the conversation about maritime modernization.
Africa, with its vast coastline, strategic trade corridors, and growing demand for port efficiency, has much to gain from AI adoption. Yet the continent faces disproportionate exposure to algorithmic risks due to limited digital infrastructure, weak regulatory oversight, and dependence on imported technologies. The lack of localized datasets, inadequate technical capacity, and fragmented legal frameworks further compound the challenge. Without proactive measures, African maritime systems risk becoming testing grounds for externally designed AI solutions that are neither context-aware nor ethically aligned with the continent’s development priorities. To navigate this path responsibly, the African maritime sector must embrace a vision of AI governance that is ethical-by-design, future-facing, and locally grounded. This requires more than regulatory mimicry; it calls for institutional innovation. Establishing a Pan-African Maritime AI Ethics Council, developing explainability protocols for high-risk systems, mandating algorithmic impact assessments, and launching regulatory sandboxes are necessary steps to build trust and resilience. Moreover, African ports and governments must invest in digital infrastructure, reclaim control over maritime data, and empower domestic innovators to design tools that reflect local realities.
International collaboration also plays a pivotal role. African states must actively engage with the International Maritime Organization (IMO), the International Electrotechnical Commission (IEC), and regional economic communities to shape global AI standards that reflect Africa’s interests. At the same time, they should leverage the African Continental Free Trade Area (AfCFTA) Digital Trade Protocol to harmonize digital policies and promote continental integration of AI-enabled maritime systems. As African nations move toward greater trade integration and green port development, responsible AI deployment must be viewed as a strategic enabler rather than a technological afterthought. Ultimately, embedding ethics and accountability into maritime AI is not just a policy imperative—it is a development necessity. The digital future of African ports must be inclusive, sovereign, and transparent, ensuring that AI technologies serve as tools of empowerment rather than instruments of exclusion. In aligning AI governance with Africa’s Agenda 2063 and the IMO’s sustainability goals, the continent can chart a course toward maritime digitalization that is not only efficient but also equitable, secure, and human-centered. This is the time for African leadership to assert agency in the digital transformation of the seas. In shaping its own governance frameworks and leveraging both global standards and local ingenuity, Africa can define the future of maritime AI on its own terms—responsibly, sustainably, and justly.
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Dr David King Boison, a maritime and port expert, AI Consultant and Senior Fellow CIMAG. He can be contacted via email at kingdavboison@gmail.com
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