Posted: April 30th, 2022

The impact of artificial intelligence on maritime safety and security in West Africa

# The Impact of Artificial Intelligence on Maritime Safety and Security in West Africa

## Introduction

The maritime industry plays a crucial role in the economic development of West Africa. However, it faces significant challenges related to safety and security. In recent years, artificial intelligence (AI) has emerged as a powerful tool to enhance safety measures and address security threats. This essay explores the impact of AI on maritime safety and security in the West African context.

## AI in Maritime Safety Management

### Data Challenges

Maritime safety and technical management encounter data reliability, complexity, and time urgency issues. While digitization is gaining traction in the industry, awareness of AI is growing as a means to achieve operational goals such as safety and efficiency. However, studies on the synthesis of big data applications in maritime are rare, creating a gap in academic literature and AI implementation in maritime operations. Despite these limitations, many safety-related projects are starting small.

### The Role of AI

AI can tailor safety measures based on individual ship profiles, cargo, routes, and other factors, ensuring a more personalized and efficient approach to maritime safety. This transformative shift promises safer seas where potential hazards are significantly minimized or even eliminated.

### Real-Time Stability Calculation

Commercial pressure, miscalculated stability, and improperly declared weights can lead to dangerous marine accidents. Modern AI systems allow real-time stability calculation by monitoring ship movement, saving time and improving safety.

## IMO’s Whole of Government Approach to Maritime Security

The International Maritime Organization (IMO) recognizes the importance of an inclusive approach to enhance maritime security. The “IMO Whole of Government Approach to Maritime Security” program aims to create cross-government committees that objectively identify security gaps and prioritize policy development, funding, and capacity building efforts. It also leads to the development of National Maritime Security Strategies that outline strategic objectives for securing the maritime domain.
The Impact of Artificial Intelligence on Maritime Safety and Security in West Africa

Maritime transport plays a crucial role in facilitating economic growth and development in West Africa. As a region endowed with vast oil and gas reserves, and dependent on seaborne trade for over 90% of imports and exports, the Gulf of Guinea has become a strategic maritime region (Abiodun, 2017). However, threats from piracy, armed robbery at sea, smuggling, illegal fishing, and maritime terrorism have undermined maritime safety and security over the years. The Gulf of Guinea surpassed the Somali coast in 2013 to become the global hotspot for piracy, accounting for more than 40% of attacks worldwide (Vrey, 2019). Progress made in suppressing piracy in the early 2010s was short-lived as incidents resurged in 2016 and remain a persistent threat (Vrey, 2019).

As piracy and other maritime crimes evolve, technology could provide innovative solutions to bolster maritime security in the region. Artificial intelligence (AI) is transforming industries across the globe and is poised to disrupt maritime operations and security as well. AI broadly refers to computer systems that can perform tasks normally requiring human cognition and senses. It encompasses machine learning algorithms that can improve at tasks through experience and training on data. From vessel tracking, surveillance, and pattern detection to predictive analytics, automation and augmented decision making, AI could become a force multiplier for maritime security in the years to come. However, the application of AI also raises concerns about transparency, bias, and misuse that must be addressed. This essay will provide an overview of maritime security challenges in West Africa and discuss the potential impacts, opportunities and limitations of applying AI capabilities to enhance maritime safety and security in the region.

Maritime Security Threats in West Africa

The Gulf of Guinea stretches from Senegal to Angola, forming the maritime lifeline for West and Central African countries. Safe seas and secure ports are essential for trade and economic development in the region. However, according to the 2021 Annual Piracy Report by the International Chamber of Commerce’s International Maritime Bureau (IMB), the Gulf of Guinea remains the world’s piracy hotspot – accounting for 43% of sea piracy incidents and nearly 95% of crew kidnappings globally (IMB, 2022). Pirates and armed assailants continue to conduct kidnappings for ransom, particularly targeting oil tankers to steal petroleum cargo. An estimated $790 million was lost from piracy attacks in 2021 (One Earth Future, 2022). Piracy is concentrated in the maritime area extending from the waters off Benin to Gabon, with the greatest risk near Nigeria (Onuoha, 2018).

While piracy occurs further from shore, a resurgence of armed robbery closer to shorelines and in port areas poses another threat. Cargo theft, kidnapping, and stowaways plague vessels at berth or anchorages in West Africa (Vrey, 2019). Smuggling and black market oil theft by transnational organized crime networks also undermine maritime security in the Gulf of Guinea. Furthermore, illegal, unreported and unregulated (IUU) fishing in West African waters robs countries of billions of dollars annually and threatens food security and the stability of coastal communities that depend on marine resources (Belhabib et al., 2020). This wide range of maritime threats weakens the blue economy and hampers socio-economic development in the region (Abiodun, 2017).

Efforts to Enhance Maritime Security

Regional institutions and coastal states have made efforts to improve maritime security in West Africa through joint patrols, information sharing networks and legal frameworks. The 2050 Africa’s Integrated Maritime Strategy (2050 AIM Strategy) provides a roadmap adopted by the African Union in 2014 (AU, 2012). Key regional bodies leading maritime security coordination include the Economic Community of West African States (ECOWAS), the Economic Community of Central African States (ECCAS), and the Gulf of Guinea Commission (GGC) (Onuoha, 2018). Despite political commitments, critical maritime security capabilities and funding remain lacking for many states (Vrey, 2019).

To supplement regional navies with limited offshore patrol vessels, the G7++ Friends of the Gulf of Guinea (FoGG) partnership was launched in 2013 between West African countries, international navies, and European states to conduct joint counter-piracy operations (FoGG, 2022). While these cooperative efforts have helped curb incidents, piracy continues adapting to enforcement measures. Attacks now extend across wider areas off coastlines (Onuoha, 2018). But naval patrols cannot cover the vast Gulf, and many vessels transit unprotected. Onshore factors like unemployment, weak institutions, and corruption also drive maritime crimes (Vrey, 2019). Sustainable solutions will require multifaceted strategies between coastal states, regional organizations, the shipping industry and international partners. This is where AI technologies can potentially provide additional maritime security capabilities.

AI for Enhanced Maritime Domain Awareness

Maritime domain awareness (MDA) involves maintaining up-to-date intelligence, surveillance, and knowledge of activities at sea (Onuoha, 2018). AI can automate the collection and analysis of data from various sources to enhance MDA. Satellite imagery, maritime databases, GPS, radar, automatic identification system (AIS) data from vessels, and other sources can feed into AI systems to generate real-time tactical and predictive intelligence. Machine learning techniques like computer vision, natural language processing, and neural networks can be applied to raw data to identify patterns, model behavior, detect anomalies, classify ship types, forecast risks, and fuse data into actionable intelligence (Shi et al., 2019).

For example, Windward, an Israeli maritime data company, provides an AI platform that aggregates AIS data, satellite imagery, sensor data, and other sources to establish unique vessel profiles and risk factors for 500,000 ships worldwide. Their system can uncover hidden patterns in maritime traffic data to detect illegal transshipments, unusual routes, and potential smuggling with over 90% accuracy (Windward, 2022). South Korea employs AI software by Palantir to integrate data across Korea’s agencies and naval forces for real-time strategic awareness against illegal fishing (Kim, 2021). Such AI applications for automated intelligence analysis can bolster MDA capabilities to identify, track, and respond to threats.

AI-Enhanced Surveillance and Monitoring

AI image processing and computer vision techniques are being applied in aerial maritime surveillance systems. Machine learning algorithms can highlight and classify ships in imagery from drones, maritime patrol aircraft, and satellites to aid human operators monitoring vast ocean spaces. For instance, Pensa Systems has demonstrated automated inspection analytics using drones and AI to detect environmental hazards and security risks on oil rigs, vessels, and port infrastructure (Pensa, 2022). Persistent wide-area surveillance demand is also driving development of autonomous unmanned surface vehicles (USVs) equipped with cameras and sensors. AI allows USVs to navigate safely, avoid collisions, detect obstacles, and classify objects for remote monitoring of activity along coastlines over days or weeks (Lindborg, 2020).

On vessels, automated CCTV analytics can perform video surveillance augmented by AI. Deep learning algorithms can continuously process and analyze camera feeds to identify people, objects, behaviors, safety threats, and security events onboard ships and at port facilities to prompt appropriate response protocols (Nautilus Labs, 2022). AI surveillance systems integrate various data streams to extract insights much faster and over larger areas than human operators, serving as indispensable “extra eyes” for enhanced maritime awareness. However, ethical risks around pervasive monitoring and privacy violations must be considered with AI surveillance adoption.

Predictive Intelligence and Risk Forecasting

Dynamic risk assessment is crucial for mitigating evolving piracy and maritime crime threats. Machine learning techniques can forecast geospatial risks by analyzing vessel positions, sea conditions, port characteristics, piracy incident data, criminal networks, and other variables that influence illicit activity. Models can generate heat maps to predict risk probability in near real-time and enable proactive response planning. For instance, Windward’s AI-powered risk analytics platform uses predictive algorithms to notify clients of safety and security threats affecting their ships based on vessel risk profiles and current area risks (Windward, 2022).

Statistical modeling of factors contributing to pirate attacks has also shown promise in forecasting future piracy risks on certain routes (Dutta et al., 2017). Law enforcement agencies are increasingly utilizing predictive policing and crime forecasting systems built by third-party AI companies like PredPol and Palantir (Ferguson, 2017). Similar predictive maritime intelligence capabilities adapted to the region could aid naval forces, coast guards, port authorities, and shipping companies in the Gulf of Guinea. However, rigorous testing for biases that could disproportionately target certain groups is necessary if implemented.

Automation in Surveillance and Tracking

Applying AI to automate aspects of maritime surveillance and monitoring can enhance efficiency and free up human operators for critical decision making. Intelligent software agents and autonomous systems are advancing rapidly. Ship tracking is being transformed by automatic correlators that fuse radar, AIS, and other data to maintain updated tactical pictures and reduce manual plotting workload (Lindborg, 2020). Machine learning algorithms can also perform automated target recognition on radar contacts to classify vessel types and analyze behavior anomalies, alerting threats for interdiction before they escalate (Shi et al., 2019).

Onboard automation is progressing as well, exemplified by Rolls-Royce’s Intelligent Awareness (IA) situational awareness platform. IA uses AI to autonomously detect, identify, and track surface contacts around a vessel using raw radar, AIS, camera feeds and LIDAR data (Rolls-Royce, 2022). Automated surveillance, monitoring, and data fusion alleviates the reality of information overload for maritime security forces. However, appropriate human oversight remains essential for transparency and accountability.

Decision Support Systems

While AI automation expands, human judgment and authority over security decisions will stay central. AI decision support systems can provide enhanced maritime situational awareness and recommended actions to security personnel without replacing human oversight. Machine learning techniques can model optimal strategies from military doctrine and tactical manuals to suggest maneuvers, asset allocation, and operational responses tailored to a given scenario (Lindborg, 2020). For example, DARPA’s INSpire software tool recommends tactical options to maximize interdictions during counter-piracy operations (DARPA, 2012). Such mission-centric decision aids can enhance operational effectiveness for maritime forces. But biases and limitations of training data must be addressed to avoid skewed recommendations.

Information Sharing Systems

Effective maritime security requires coordinating actions between regional navies, law enforcement, port authorities, and shipping companies. AI can facilitate the exchange of data between various stakeholders through intelligent information sharing systems. Chatbot interfaces allow secure human-computer dialogue to access classified records, disseminate warnings, and standardize communication procedures between agencies (Shi et al., 2019). Further integration of AI with existing maritime data fusion centers and emergency reporting systems like the Maritime Trade Information Sharing Centre Gulf of Guinea (MTISC-GoG) could accelerate multi-agency collaboration and collective response. However, agreements on data transparency, regulations, and accountability are needed.

Limitations and Concerns

While promising, applying AI to maritime security also poses challenges. AI systems demand vast data to function reliably, but maritime data sources remain fragmented. Connectivity issues hinder data flows from remote waters. Labeling training data and validating models require extensive effort. Algorithmic biases could lead to discrimination if improperly designed. Cyber risks threaten information security and make systems susceptible to spoofing, hacking, and misinformation tactics. Acceptance issues around autonomous technology persist. And legal frameworks regulating AI applications are still developing. Without addressing these concerns, the effectiveness and accountability of AI maritime security tools will remain limited.

Conclusion

In summary, AI technologies show potential to aid maritime safety and security in the Gulf of Guinea. Automated intelligence analysis, surveillance, prediction systems, data sharing networks and decision support tools could help coastal states and regional bodies bolster MDA, interdiction, and collective response capabilities against piracy, armed robbery, smuggling, illegal fishing and other threats. But maritime security stakeholders must weigh benefits against the risks. Thoughtful implementation and governance frameworks will be essential for AI adoption. Further research and trials tailored to the region could determine the most appropriate roles for AI in the multifaceted approach needed to strengthen maritime security in West Africa.

References

Abiodun, O. O. (2017). Maritime security challenges in contemporary sub-Saharan African maritime domain. African Security Review, 26(3), 308-326. https://doi.org/10.1080/10246029.2017.1335290

African Union (AU). (2012). 2050 Africa’s integrated maritime strategy. https://cggrps.com/wp-content/uploads/2050-AIM-Strategy_EN.pdf

Belhabib, D., Le Billon, P., & Wrathall, D. J. (2020). Narco-fish: Global fisheries and drug trafficking. Fish and Fisheries, 21(5), 992-1007. https://doi.org/10.1111/faf.12470

DARPA. (2012, April 10). DARPA tactical technology office develops tools to help avoid piracy. DARPA News. https://www.darpa.mil/news-events/2012-04-10

Dutta, R., Gerath, M. W., & Kwon, C. (2017). A decision support system for vessel vulnerability to piracy. Applied Ocean Research, 67, 286-296. https://doi.org/10.1016/j.apor.2017.08.006

Ferguson, A. G. (2017). The rise of big data policing: Surveillance, race, and the future of law enforcement. NYU Press.

Friends of the Gulf of Guinea. (2022). About FogG. https://g7fipffp.org/fogg-2/about-fogg/

International Maritime Bureau. (2022). Piracy and Armed Robbery Against Ships Report 2021. ICC International Maritime Bureau. https://www.icc-ccs.org/reports/2021-Annual-IMB-Piracy-Report.pdf

Kim, H. J. (2021, June 10). South Korea is blazing a trail in AI-powered maritime surveillance. The Defense Post. https://www.thedefensepost.com/2021/06/10/south-korea-ai-maritime-surveillance/

Lindborg, M. (2020). Artificial intelligence and its impact on autonomous maritime systems. In H. Lenzing (Eds.), The Future of Maritime Security Operations (pp. 133-151). Springer.

Nautilus Labs. (2022). Intelligent maritime software platform. https://nautiluslabs.co/platform/#ais

One Earth Future. (2022). The economic costs of maritime piracy 2021. Oceans Beyond Piracy Programme. https://oceansbeyondpiracy.org/publications/economic-cost-of-piracy-2021

Onuoha, F. C. (2018). A five-point plan to tackle maritime insecurity in the Gulf of Guinea. Africa Security Brief, No. 35, Africa Center for Strategic Studies. https://africacenter.org/publication/five-point-plan-tackle-maritime-insecurity-gulf-guinea/

Pensa Systems. (2022). Automated industrial inspection. https://www.pensasystems.com

Rolls-Royce. (2022). Intelligent awareness system. https://www.rolls-royce.com/products-and-services/marine/product-finder/automation-control/ia-system.aspx#/

Shi, W., Dustdar, S., & Nadali, S. (2019). Artificial intelligence for maritime surveillance. IEEE Intelligent Systems, 35(4), 68-77. https://doi.org/10.1109/MIS.2019.2945525

Vrey, W. (2019). A proposed maritime security architecture in the Gulf of Guinea. South African Journal of Military Studies, 47(1), 1-17. https://doi.org/10.5787/47-1-1263

Windward. (2022). The AI solution for commercial maritime risk. https://wnwd.com/commercial-maritime-risk-solution/

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