Abstract Arbitration proceedings in India are adversely affected by multiple adjournments and procedural uncertainties in the courts. To ensure limited judicial intervention, certain procedural tasks can be automated via artificial intelligence (AI) to improve the efficiency of the arbitration process. The objective of the paper is to examine the current state of the Indian judicial framework and assess the procedural feasibility of incorporating AI to address arbitration-related limitations. The research employs a thematic review of the literature to assess the use of AI in dispute resolution proceedings. In addition, using a comparative research methodology, the study critically analyzes the technological and policy-related initiatives taken by both India and China in their respective judicial systems. Findings suggest that reliance on the party autonomy principle in arbitration is a solution to the limitations that disincentivize governments from implementing AI-powered automation in dispute resolution. The paper, therefore, recommends an integrative approach to AI technology that partially automates arbitration proceedings in the courts.
Abstract Legal documents are widely reputed to be long and complex, making the manual identification of key topics time-consuming and resource intensive. However, due to the recent advent of large language models (LLMs), topic modeling has become more accessible and easier. This study aims to efficiently extract topics from eminent domain adjudications of the Land Tribunal in South Korea by using two popular topic extraction models: Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers Topic (BERTopic). We evaluate the topics identified by LDA and BERTopic using both quantitative (topic coherence metric) and qualitative (domain-specific knowledge) methods. The qualitative approach yielded a more reliable assessment than the quantitative one, revealing that BERTopic performed best when applied to English adjudication text. This model extracted eight meaningful topics, including land compensation, farming and fishing compensation, appeals for exclusion, and relocation issues. For legal information professionals, the findings demonstrate how advanced topic modeling can streamline the retrieval and analysis of legal texts, enabling efficient access to precedents and ultimately supporting informed decision-making in specialized domains.
Abstract This column profiles Theresa Buller, a law librarian at the University of Canterbury, and examines law librarianship in Aotearoa New Zealand. It explores the shift to online legal research, the impact of AI and commercial databases, and the role of tikanga Māori within a bicultural legal system. The piece also highlights collaborative open educational resources supporting legal research education.
Abstract Legal research in Indonesia and Malaysia continues to evolve amid growing demands for methodological transparency and analytical rigor. However, the dominance of doctrinal approaches has limited the adoption of empirical and mixed methods in regional legal scholarship. This study aims to examine the implementation of data analysis methods in Scopus-indexed law journals from both countries between 2019 and 2024. Using a comparative qualitative approach supported by content analysis, 60 research articles were systematically reviewed from four selected journals. The findings reveal that qualitative methods remain predominant (78 %), while mixed methods are emerging gradually, especially in Malaysian publications. Quantitative techniques are used minimally (3 %), indicating persistent reliance on document-based normative research. Differences between Indonesia and Malaysia are influenced by academic traditions, legal education systems, and editorial policies. This study is original in mapping the methodological patterns of legal research in two Southeast Asian jurisdictions through an empirical review of Scopus-indexed publications. The results contribute to the enhancement of methodological literacy and highlight the need for integrating normative and empirical approaches in legal studies. Strengthening mixed-method applications is essential to advancing evidence-based legal scholarship and aligning Indonesian and Malaysian legal research with global academic standards.
Abstract This study aims to examine the role of Artificial Intelligence (AI) in crypto trading and the legal and technical issues that arise, as well as to formulate an ideal regulatory and supervisory framework. The method used is a doctrinal approach with comparison, comparing Indonesia and the European Union (EU) to evaluate the applicable regulations and supervision. The results show that the current regulatory and supervisory framework is inadequate to address the challenges of AI auditing in cryptocurrency trading. The EU regulates a risk-based approach, algorithmic auditing, and product liability regulations relevant to AI governance. Meanwhile, AI regulations in Indonesia are still under development and implement a centralized strategy led by the government, linking AI application to national strategic objectives, and are currently based on AI Ethics Guidelines. In terms of regulatory compliance, there is a need to develop and harmonize structured AI audit standards and AI transparency, as well as implement efficient regulatory technology (RegTech) and supervisory technology (SupTech), such as the Financial Services Authority SupTech Integrated Data Analytics (OSIDA) system. There is a need to strengthen and harmonize technology-based legal and supervisory frameworks so that AI in crypto trading becomes transparent and accountable.
