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This paper explores the lawyer licensing from a political economy perspective of rent-seeking theory, departing from the traditional legal approach. For the purpose of study, we selected four subjects - the introduction of law school system, conflicts with legal-adjacent professions, the legal market opening, and legal-tech conflicts - through semantic network analysis. To compare the short-term social waste due to rent-seeking in these subjects, we derived criteria of competitiveness, openness, and complexity based on Tullock's efficient rent-seeking theory. The results show that the increase in the number of lawyers due to the introduction of law schools and the resulting conflicts with legal-adjacent professions led to social waste in the lawyer licensing. In contrast, social waste from legal-tech conflicts showed little short-term change, while the one from legal market opening with entry barriers actually decreased. This demonstrates that long-term efforts to minimize rent can create short-term conflicts and social costs, potentially leading to unintended consequences. Therefore, to minimize social waste, policies should be designed considering short-term efficiency while removing rent in the long-term, such as reducing the number of rent-seekers and limiting the power of interest groups. Despite methodological limitations, this study is significant as a preliminary application of efficient rent-seeking theory to the lawyer licensing, showing potential for future research in various fields.

This study explores the applicability of generative artificial intelligence (Generative AI), particularly ChatGPT, to legal information retrieval and regulatory classification. Two tasks were designed: one involved generating legal provisions based on input information such as statute name, article number, and title; the other involved classifying whether a given provision is regulatory or non-regulatory. The study evaluated classification accuracy, content similarity, execution consistency, and performance variation across conditions. Results showed that in the provision generation task, ChatGPT often produced inaccurate, inconsistent, or hallucinated content, with low similarity to reference texts. In contrast, the classification task achieved a mean accuracy of 66.9%, with high consistency and reliability, suggesting potential utility as a supportive tool for identifying regulatory provisions. However, the frequent misclassification of non-regulatory provisions remains a key limitation. As an initial empirical assessment of generative AI in legal and regulatory contexts, this study provides a foundation for future research comparing domain-specific AI models and exploring their use in regulatory support tasks.

Regulatory sandboxes have served as institutional tools that allow new products and services based on emerging technologies to be introduced into the market under limited conditions, granting temporary exemptions from existing regulations. These trials are used to test and validate innovations, and the results are reflected as a basis for regulatory improvement and broader market adoption. In recent years, as artificial intelligence (AI) technologies have increasingly converged with various industries, AI-related businesses have been designated across multiple regulatory sandboxes in Korea, including those for industrial convergence, ICT, smart cities, and innovative finance. However, the current system faces structural challenges. Each sandbox is managed by a different government ministry based on the industry sector, which can result in a mismatch between the technical characteristics of the proposed innovation and the regulatory expertise of the overseeing authority. This misalignment can lead to delays in regulatory improvements or impose excessive compliance conditions that undermine the commercial viability of new technologies. To explore alternatives to these limitations, this study examines Singapore’s AI-specific regulatory sandbox model, which is led by the Infocomm Media Development Authority (IMDA). IMDA operates a sandbox to assess the safety and trustworthiness of generative AI systems, aiming to establish a structured framework for pre-deployment testing and evaluation. At the same time, it also runs sandboxes designed to encourage AI adoption among small and medium-sized enterprises (SMEs). This dual structure—combining government-led regulatory design with participation from the private sector—is regarded as an effective strategy to bridge emerging technologies with responsive regulatory frameworks. The paper argues that Singapore’s model offers valuable insights for Korea, especially in light of the recently enacted Basic Law on AI. To move beyond fragmented, sector-specific regulation, Korea should establish a dedicated sandbox framework for general-purpose technologies like AI, incorporate top-down regulatory planning, and build an integrated management system under a centralized regulatory authority. By grounding regulatory design in empirical evidence from pilot testing, Korea’s regulatory sandbox system can evolve into a dynamic and strategic platform capable of addressing the wide-ranging risks and impacts of advanced AI technologies.