Governing AI in Public HRM: A Critical Analysis of Taiwan’s Draft Artificial Intelligence Basic Law

Yu-Sheng Yang

doi:10.18196/jgpp.v12i3.26878

Authors

Yu-Sheng Yang Institute of Political Economy, College of Social Sciences, National Cheng Kung University, Taiwan

DOI:

https://doi.org/10.18196/jgpp.v12i3.26878

Keywords:

Public human resource management, Data privacy, Algorithmic fairness, Decision transparency, Accountability

Abstract

This study evaluates the suitability of the Draft Artificial Intelligence Basic Law (2024) for public human resource management (PHRM) in Taiwan, focusing on data privacy, algorithmic fairness, decision transparency, and accountability. PHRM involves recruitment, evaluation, and appointment processes that extensively apply personal data and algorithms, entailing significant legal and ethical risks. Using qualitative methods, this study compares Taiwan’s approach with the EU’s risk-based and the US’s market-driven models. Triangulation and institutional analysis are employed to assess the draft’s provisions on legitimacy, fairness, and accountability. Findings showed the draft omits key rights such as data portability, the right to be forgotten, and data protection impact assessments (DPIA), and lacks algorithm audits, disclosure, and appeal mechanisms. These gaps may lead to bias, opacity, and violations of rights, with risks amplified under conditions of regulatory flexibility. The novelty of this study lies in its integration of AI governance with the specific context of public human resource management in Taiwan, an area where legal-ethical risks are high but underexplored in existing literature. Unlike prior research that mainly addresses AI governance in commercial or general administrative domains, this study highlights how the unique features of PHRM—such as recruitment algorithms and performance evaluation systems—intersect with data rights and accountability requirements. By situating the Draft AI Law within this sensitive policy arena, the study extends ICT adoption theories beyond traditional models emphasizing usefulness and ease of use, foregrounding public values, ethical safeguards, and institutional legitimacy. From a policy perspective, this study recommends strengthening data rights, establishing compliance and audit systems, creating independent regulatory bodies, and implementing disclosure requirements, thereby providing both theoretical and practical insights for AI governance in Taiwan and the broader region.

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Governing AI in Public HRM: A Critical Analysis of Taiwan’s Draft Artificial Intelligence Basic Law

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