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The Role of Artificial Intelligence in Immigration Law Enforcement: Balancing Efficiency, Transparency, and Ethical Accountability Muhammad Arief Hamdi; Bobby Briando; Faisal Santiago
Journal of Multidisciplinary Sustainability Asean Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v1i6.1835

Abstract

Background. The integration of Artificial Intelligence (AI) in immigration law enforcement has significantly improved efficiency in areas such as fraud detection, border security, and visa application assessments. However, the implementation of AI raises critical concerns related to transparency, fairness, and ethical accountability. The "black box" nature of AI systems often obscures the reasoning behind decisions, posing risks to the rights of migrants, especially refugees and asylum seekers. Furthermore, the increased use of biometric data for security purposes heightens privacy concerns and potential misuse. Purpose. This study aims to analyze the role of AI in immigration law enforcement, focusing on its benefits, limitations, and ethical challenges. It seeks to provide recommendations for regulatory frameworks that ensure a balance between operational efficiency and the protection of human rights. Method. The research adopts a qualitative approach, combining a review of scholarly articles and case studies from journals such as Comparative Migration Studies and AI & Society. Key themes include transparency, fairness, privacy, and accountability. Results. AI significantly enhances operational efficiency but remains vulnerable to biases and errors that can disproportionately affect vulnerable populations. Human oversight is critical to ensuring ethical decision-making and maintaining accountability. Conclusion. The integration of AI in immigration law must be guided by transparent, fair, and ethical regulatory frameworks. Emphasizing human oversight ensures that moral responsibility remains with human actors rather than AI systems.