AI-Based Public Service Transformation: An Analysis of Algorithmic Bias Risks and Digital Government Accountability in ASEAN

Authors

  • Adelia Idil Department of Public Administration, University of Malang Author
  • Huda Alimuddin Department of Public Administration, University of Malang Author

DOI:

https://doi.org/10.71435/

Keywords:

AI Governance , Algorithmic Bias , Asean Countries , Public Trust

Abstract

Purpose: The rapid integration of Artificial Intelligence (AI) in public service delivery has brought both opportunities and challenges in the ASEAN region. This study investigates the risks of algorithmic bias and the effectiveness of digital government accountability in five ASEAN countries: Singapore, Indonesia, Malaysia, Thailand, and the Philippines.

Subjects and Methods: Through expert interviews, policy document analysis, and comparative case studies, the research identifies key issues such as biased algorithms in welfare programs and predictive policing, as well as the varying levels of institutional accountability across countries. Singapore’s comprehensive AI governance framework, emphasizing transparency and fairness, contrasts with the more fragmented approaches in Indonesia and the Philippines, where AI systems often lack oversight mechanisms.

Results: The study finds that public trust in AI-based public services is strongly influenced by transparency and citizen engagement. It concludes that addressing algorithmic bias and strengthening accountability mechanisms are essential for the responsible deployment of AI in public services.

Conclusions: This research contributes to the existing literature on AI governance, particularly within the ASEAN context, and calls for further exploration of AI governance frameworks and citizen participation in less mature AI environments.

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Published

2024-12-12

How to Cite

AI-Based Public Service Transformation: An Analysis of Algorithmic Bias Risks and Digital Government Accountability in ASEAN. (2024). Asian Digital Governance Problems, 1(2), 78-87. https://doi.org/10.71435/