The Use of AI in Maritime Delimitation Addressing the Equity Principle and Judicial Subjectivity

Refia Nur Yagmur

Chapter 8 in AI-Driven Revolution:Transforming the Business Landscape, 2025, pp 143-165 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This chapter explores the potential of Artificial Intelligence (AI) to transform maritime delimitation, a complex process that balances geographical, legal, and equitable considerations, to resolve disputes over maritime boundaries. While traditional maritime delimitation relies on human adjudication, AI offers opportunities to enhance efficiency, consistency, and objectivity through its data-processing capabilities. However, integrating AI into this nuanced legal domain raises significant challenges, including algorithmic bias, opacity, and the difficulty of translating equitable principles into computational frameworks. The chapter begins with a literature review. It then examines the legal and technical foundations of maritime delimitation, emphasizing the role of equity and the evolution of methodologies such as the equidistance principle and the three-stage approach. The discussion shifts to AI’s potential applications, including machine learning and natural language processing, which could support decision-making by analyzing vast datasets and identifying patterns in legal precedents. However, the chapter also critically evaluates the limitations of AI, particularly its inability to fully grasp the contextual and interpretative nature of legal reasoning, which is essential for achieving equitable outcomes. Ethical concerns, such as data bias, model fragility, and the risk of judicial conformism, are also addressed, underscoring the need for transparency and human oversight. The chapter concludes by advocating for a balanced approach, where AI serves as a decision-support tool rather than a replacement for human judges.

Keywords: Artificial Intelligence; Data Analytics; AI; Digital Landscape; Organizational Strategies; AI Technologies; Machine Learning; Natural Language Processing; Robotics; Digital Transformation; Business Models; Efficiency; Value Propositions; Advanced Analytics; Predictive Modelling; Customer Experiences; AI-driven; Ethical AI; Data Privacy; Algorithmic Bias; Regulation Compliance; Responsible AI; Sustainable AI; Practical Applications; Business Innovation; Emerging Technologies; Industry 4.0; High Tech; Ethics Regulation; Business Leadership; Pattern Recognition; Information Technology; Entrepreneurs; Management (search for similar items in EconPapers)
JEL-codes: L1 L2 L21 L26 M1 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.worldscientific.com/doi/pdf/10.1142/9781800616578_0008 (application/pdf)
https://www.worldscientific.com/doi/abs/10.1142/9781800616578_0008 (text/html)
Ebook Access is available upon purchase.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9781800616578_0008

Ordering information: This item can be ordered from

Access Statistics for this chapter

More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().