Abstract
Abstract This Article examines Taiwan’s newly enacted Basic Law on Artificial Intelligence, which entered into force on January 14, 2026, and situates it within the rapidly evolving and fragmented landscape of global AI governance. Although Taiwan plays a critical role in the global AI ecosystem, it remains underrepresented in international regulatory discourse. Addressing this gap, the Article analyzes Taiwan’s regulatory approach against leading international models, particularly the European Union’s AI Act and the US NIST AI Risk Management Framework, while also drawing limited comparative insights from regional jurisdictions such as Japan, South Korea, and Singapore. The Article argues that Taiwan has adopted a hybrid, risk-based co-regulatory framework that combines principle-based legislation with delegated technical standard-setting, reflecting an effort to balance innovation with the protection of fundamental rights. While informed by foreign models, Taiwan’s approach avoids wholesale transplantation and instead reflects its distinct institutional and policy context. Through doctrinal and comparative analysis, the Article identifies key ambiguities in the AI Basic Law, including the scope and operationalization of transparency obligations, the allocation of regulatory authority across government bodies, the definition of high-risk AI systems, the treatment of general-purpose AI, and the limited scope of data governance provisions. To address these challenges, the Article advances four principal recommendations: clarifying and operationalizing transparency requirements; strengthening whole-of-government coordination; refining risk classification, particularly with respect to high-risk and general-purpose AI; and expanding data governance beyond personal data to encompass broader informational assets. By evaluating Taiwan’s emerging regulatory framework, this Article contributes to ongoing debates on AI governance and offers insights into how innovation-driven jurisdictions can design context-sensitive, accountable, and adaptable approaches to AI regulation.