Artificial Intelligence in Malaysian Criminal Sentencing: Reassessing Due Process and Judicial Accountability
DOI:
https://doi.org/10.37253/conescintech.v6i1.12701Keywords:
Artificial Intelligence, Criminal Sentencing, Algorithmic AccountabilityAbstract
The integration of artificial intelligence (AI) into criminal sentencing has increasingly influenced decision-making within contemporary criminal justice systems. In Malaysia, the implementation of the Artificial Intelligence Sentencing System (AISS) in Sabah and Sarawak courts has intensified debates concerning the compatibility of AI-assisted sentencing with the rule of law, judicial discretion and constitutional safeguards. Although AI-powered sentencing tools are promoted as mechanisms capable of improving efficiency, consistency and predictive assessment, their application raises serious concerns relating to transparency, explainability, procedural fairness and algorithmic bias. The absence of a specific Malaysian legal framework regulating AI-assisted sentencing further creates uncertainty regarding the extent to which courts may rely on algorithmic recommendations without compromising the accused person’s constitutional rights under Articles 5 and 8 of the Federal Constitution. Accordingly, this study examines the legal implications of AI-assisted criminal sentencing in Malaysia through a doctrinal legal research methodology. The research analyses relevant statutory provisions, including the Penal Code (Act 574), Criminal Procedure Code (Act 593), Subordinate Courts Act 1948. The study finds that excessive reliance on opaque algorithmic systems may undermine natural justice, weaken judicial reasoning and challenge the principles underpinning the rule of law. This article argues that AI should operate solely as a supplementary judicial aid subject to strict human oversight, transparency requirements and explainability safeguards within Malaysia’s criminal justice framework.
Downloads
References
Casey, B., Farhangi, A., & Vogl, R. (2019). Rethinking explainable machines: The GDPR’s right to explanation debate and the rise of algorithmic audits in enterprise. Berkeley Technology Law Journal, 34(1), 143–188. https://btlj.org/data/articles2019/34_1/04_Casey_Web.pdf
Citron, D. K. (2008). Technological due process. Washington University Law Review, 85(6),1249–1313. https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1166&context=law_lawreview
Criminal Procedure Code (Act 593). (2017). Laws of Malaysia: Criminal Procedure Code (Act 593). https://www.forestry.gov.my/images/stories/pdf/Akta/Kanun%20Tatacara%20Jenayah%20CPC-%20BI.pdf
European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
Federal Constitution. (2016). Federal Constitution. https://www.rcrc-resilience-southeastasia.org/wp-content/uploads/2017/12/2010_federal_constitution__2016_version_.pdf
Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741
Khong, D. W. K., & Ho, C. C. (2022). Artificial Intelligence in Malaysian Courts: PP v Denis P Modili. Asian Journal of Law and Policy, 2(2), 127–136. https://doi.org/10.33093/ajlp.2022.9
Lim, C., & Gong, R. (2020, August 18). Artificial intelligence in the courts: AI sentencing in Sabah and Sarawak. Khazanah Research Institute. https://old.krinstitute.org/Views-%40-Artificial_Intelligence_in_the_Courts-%3B_AI_sentencing_in_Sabah_and_Sarawak.aspx
Malgieri, G., & Comandé, G. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(4), 243–265. https://doi.org/10.1093/idpl/ipx005
Mayson, S. G. (2019). Bias in, bias out. Yale Law Journal, 128, 2218–2300. https://yalelawjournal.org/pdf/Mayson_p5g2tz2m.pdf
Meyer, M., Horowitz, A., Marshall, E., & Lum, K. (2022). Flipping the script on criminal justice risk assessment: An actuarial model for assessing the risk the federal sentencing system poses to defendants. arXiv. https://arxiv.org/abs/2205.13505
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87
Oswald, M. (2018). Algorithm-assisted decision-making in the public sector. Philosophical Transactions of the Royal Society A, 376(2128). https://doi.org/10.1098/rsta.2017.0359
Penal Code (Act 574). (2023). Laws of Malaysia: Penal Code (Act 574).
https://www.sprm.gov.my/admin/files/sprm/assets/pdf/penguatkuasaan/Akta_574_%2831.5.2023%29_BI.pdf
Sanghvi, H. (2022). Application of artificial intelligence in the sentencing process. In Proceedings of the International Conference on Law and Digital Governance. Atlantis Press. https://doi.org/10.2991/assehr.k.220105.012
Subordinate Courts Act 1948 (Act 92) (Malaysia). (2025). https://tcclaw.com.my/wp-content/uploads/2025/08/Subordinate-Courts-Act-1948.pdf
Toh, J., Lee, A., & Partners. (2023). Artificial intelligence and judicial decision-making: Emerging legal challenges in common law jurisdictions. Legal Practice Review, 15(2), 45–68. https://ssm-italia.eu/wp-content/uploads/2025/02/JuLIA_handbook-Justice_final.pdf
Verma, S., & Rubin, J. (2018). Fairness definitions explained. In Proceedings of the International Workshop on Software Fairness (pp. 1–7). https://doi.org/10.1145/3194770.3194776
Zainol, Z., Abdul Rahman, N. A., & Mohamed, M. F. (2023). Artificial intelligence-powered criminal sentencing in Malaysia. Environment-Behaviour Proceedings Journal, 8(24), 89–96. https://ebpj.e-iph.co.uk/index.php/EBProceedings/article/view/3813/2118
Završnik, A. (2020). Criminal justice, artificial intelligence systems, and human rights. ERA Forum, 20(4), 567–583. https://doi.org/10.1007/s12027-020-00602-0
Zilka, M., Fogliato, R., Hron, J., Butcher, B., Ashurst, C., & Weller, A. (2023). The progression of disparities within the criminal justice system: Differential enforcement and risk assessment instruments. arXiv. https://arxiv.org/abs/2305.07575
Ziosi, M., & Pruss, D. (2024). Evidence of what, for whom? The socially contested role of algorithmic bias in a predictive policing tool. arXiv. https://arxiv.org/abs/2405.07715









