Optimizing Omnibus Law in Indonesia: A Legal Enquiry on the Use of Artificial Intelligence for Legislative Drafting

  • Zaki Priambudi University of Jember, Indonesia
  • Namira Hilda Papuani University of Jember, Indonesia
  • Ramdhan Prawira Mulya Iskandar University of Jember, Indonesia

Abstract

The omnibus law model is often regarded as a practical solution to harmonize and synchronize statutory regulations. In practice, the application of this model tends to be pragmatic and less democratic. This paper aimed to analyze the essential considerations and challenges of implementing the omnibus law model in Indonesia and the further relevance of applying Artificial Intelligence (AI) in the legislation. By combining doctrinal and socio-legal research, this paper demonstrated the potential for AI in optimizing the omnibus law model legislation. The results of this study indicated that AI could not immediately replace the role of legislative institutions and only acts as a tool and not as a determinant in the legislative process. There were two AI features that could assist legislative bodies in designing the omnibus law model. They  were document review as a construction scanner for legislation and predictive analytics as a prediction system. Both could help legislative bodies in optimizing the omnibus law model, which tended to have a high complexity level. As a follow-up, it was expected that the government could create a special institution that focused on optimizing AI-based legislation.


KEYWORDS: Omnibus Law, Artificial Intelligence, Legislation, Public Participation.

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Published
2021-03-03
How to Cite
PRIAMBUDI, Zaki; PAPUANI, Namira Hilda; ISKANDAR, Ramdhan Prawira Mulya. Optimizing Omnibus Law in Indonesia: A Legal Enquiry on the Use of Artificial Intelligence for Legislative Drafting. Indonesian Journal of Law and Society, [S.l.], v. 2, n. 1, p. 79-104, mar. 2021. ISSN 2722-4074. Available at: <https://jurnal.unej.ac.id/index.php/IJLS/article/view/21787>. Date accessed: 17 sep. 2021. doi: https://doi.org/10.19184/ijls.v2i1.21787.

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