Gauging Algorithmic Justice: Assessing Indonesia’s Readiness to Adopt Predictive Justice in Criminal Law
Keywords:
Predictive Justice; Criminal Law; Algorithmic Risk AssessmentAbstract
This article analyses the normative, institutional, and procedural readiness of Indonesia’s criminal justice system to adopt predictive justice tools, particularly algorithmic risk assessment, as decision‑support in bail, sentencing, and parole. Using a normative juridical and comparative approach, the study examines the compatibility of such tools with fundamental criminal law principles, including legality, individual culpability, presumption of innocence, proportionality, equality before the law, and the right to a fair and transparent trial. It argues that algorithmic, forward‑looking assessments of risk sit in structural tension with a system traditionally grounded in adjudication of past wrongdoing and individual guilt, and may entrench historical bias embedded in criminal justice data. At the same time, the article evaluates Indonesia’s institutional and regulatory infrastructure, identifying serious deficiencies in data quality and governance, legal rules on admissibility and transparency of algorithmic assessments, oversight mechanisms, and technical capacity among legal actors. The study concludes that Indonesia is not yet adequately prepared to integrate predictive justice and that significant reforms are required before such tools can be legitimately employed as subordinate aids rather than drivers of criminal decision‑making.
References
V. Chiao, “Fairness, Accountability and Transparency: Notes on Algorithmic Decision-Making in Criminal Justice,” International Journal of Law in Context, vol. 15, no. 2, hlm. 126–143, 2019, doi: 10.1017/s1744552319000077.
R. Jorgensen, “Algorithms and the Individual in Criminal Law,” Canadian Journal of Philosophy, vol. 52, no. 1, hlm. 61–77, 2022, doi: 10.1017/can.2021.28.
T. Sabera, “Beyond ‘Fairness’: Rethinking the Use of Algorithmic Predictions in Criminal Justice,” Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 8, no. 3, hlm. 2242–2247, 2025, doi: 10.1609/aies.v8i3.36709.
A. S. Almasoud dan J. A. Idowu, “Algorithmic Fairness in Predictive Policing,” AI and Ethics, vol. 5, no. 3, hlm. 2323–2337, 2025.
R. A. Berk, A. K. Kuchibhotla, dan E. T. Tchetgen, “Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets,” Sociological Methods & Research, vol. 53, no. 1, hlm. 3–44, 2024, doi: 10.1177/00491241231155883.
T.-W. Hung dan C.-P. Yen, “Predictive Policing and Algorithmic Fairness,” Synthese, vol. 201, no. 6, hlm. 1–25, 2023, doi: 10.1007/s11229-023-04189-0.
R. Arvitto dan P. Astuti, “Predictive Justice Sebagai Inovasi Sistem Peradilan Pidana: Studi Komparatif Indonesia Dan Belanda,” Indonesian Journal of Contemporary Law, vol. 3, no. 02, hlm. 1–20, Jan 2026.
C. Engels, L. Linhardt, dan M. Schubert, “Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism,” Artif Intell Law, vol. 33, no. 2, hlm. 383–404, Jun 2025, doi: 10.1007/s10506-024-09389-8.
J. Efendi Jonaedi, dan Ibrahim, Metode Penelitian Hukum: Normatif dan Empiris. Prenadamedia Group, 2018.
J. E. Johndrow dan K. Lum, “An Algorithm for Removing Sensitive Information: Application to Race-Independent Recidivism Prediction,” Annals of Applied Statistics, vol. 13, no. 1, hlm. 189–220, 2019, doi: 10.1214/18-AOAS1201.
J. E. Johndrow dan K. Lum, “An Algorithm for Removing Sensitive Information: Application to Race-Independent Recidivism Prediction,” Annals of Applied Statistics, vol. 13, no. 1, hlm. 189–220, 2019, doi: 10.1214/18-AOAS1201.
M. Miron, S. Tolan, E. Gómez, dan C. Castillo, “Evaluating Causes of Algorithmic Bias in Juvenile Criminal Recidivism,” Artificial Intelligence and Law, vol. 29, no. 1, hlm. 111–138, 2021, doi: 10.1007/s10506-020-09268-y.
R. Peeters dan M. Schuilenburg, “Machine Justice: Governing Security through the Bureaucracy of Algorithms,” Information Polity, vol. 23, no. 3, hlm. 267–280, 2018.
J. Ryberg, “Criminal Justice and Artificial Intelligence: How Should We Assess the Performance of Sentencing Algorithms?,” Philosophy & Technology, vol. 37, no. 1, hlm. 1–15, 2024, doi: 10.1007/s13347-024-00694-3.
A. Završnik, “Algorithmic Justice: Algorithms and Big Data in Criminal Justice Settings,” European Journal of Criminology, vol. 18, no. 5, hlm. 623–642, 2021, doi: 10.1177/1477370819876762.
A. S. Almasoud dan J. A. Idowu, “Algorithmic Fairness in Predictive Policing,” AI and Ethics, vol. 5, no. 3, hlm. 2323–2337, 2025.
R. A. Berk, A. K. Kuchibhotla, dan E. T. Tchetgen, “Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets,” Sociological Methods & Research, vol. 53, no. 1, hlm. 3–44, 2024, doi: 10.1177/00491241231155883.
R. Berk, H. Heidari, S. Jabbari, M. Kearns, dan A. Roth, “Fairness in Criminal Justice Risk Assessments: The State of the Art,” Sociological Methods & Research, vol. 50, no. 1, hlm. 3–44, 2021, doi: 10.1177/0049124118782533.
J. Dressel dan H. Farid, “The Accuracy, Fairness, and Limits of Predicting Recidivism,” Science Advances, vol. 4, no. 1, hlm. 5580, 2018, doi: 10.1126/sciadv.aao5580.
S. Kawamleh, “Algorithmic Evidence in U.S Criminal Sentencing,” AI and Ethics, vol. 4, no. 4, hlm. 1315–1328, 2024, doi: 10.1007/s43681-023-00368-4.
M. Miron, S. Tolan, E. Gómez, dan C. Castillo, “Evaluating Causes of Algorithmic Bias in Juvenile Criminal Recidivism,” Artificial Intelligence and Law, vol. 29, no. 1, hlm. 111–138, 2021, doi: 10.1007/s10506-020-09268-y.
M. Musbih dan O. Abdal, “Using Predictive Justice Algorithms in Criminal Justice System: Prospects and Challenges,” International Review of Law, vol. 10, no. 1, hlm. 233–266, 2021, doi: 10.29117/irl.2021.0161.
M. Ríos dan Pilar, “Predictive Algorithms and Criminal Justice: Expectations, Challenges and a Particular View of the Spanish VioGén System,” Rivista Italiana di Informatica e Diritto, vol. 6, no. 1, hlm. 1–18, 2024, doi: 10.32091/RIID0168.
A. Alimardani dan M. Istiqomah, “Beyond Black Boxes and Biases: Advancing Artificial Intelligence in Sentencing,” dalam Current Issues in Criminal Justice, 2025, hlm. 1–22. doi: 10.1080/10345329.2025.2527994.
R. Arvitto dan P. Astuti, “Predictive Justice sebagai Inovasi Sistem Peradilan Pidana: Studi Komparatif Indonesia dan Belanda,” Indonesian Journal of Contemporary Law, vol. 1, no. 2, hlm. 1–24, 2026.
R. Ayu, W. Karna, dan B. Wibowo, “Transformasi Penegakan Hukum di Era Kecerdasan Buatan: Analisis Normatif terhadap Kebutuhan Reformasi Regulasi Hukum Pidana dan Acara Pidana di Indonesia,” Legal Note: Jurnal Hukum dan Kebijakan Publik, vol. 2, no. 1, hlm. 1–24, 2026, doi: 10.70716/legalnote.v2i1.197.
A. Chouldechova, “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments,” Big Data, vol. 5, no. 2, hlm. 153–163, 2017, doi: 10.1089/big.2016.0047.
C. S. A. T. Lesmana, D. Y. Pebrianto, dan A. Erfina, “Keadilan Mesin: Telaah Filosofis atas Legitimasi Keputusan Hukum Berbasis Teknologi,” Rechten: Riset Hukum dan Hak Asasi Manusia, vol. 6, no. 2, hlm. 1–18, 2024, doi: 10.52005/rechten.v6i2.177.
R. Rusmiyati, “Advancing Law Enforcement Efficiency Through Predictive Policing Technologies,” Jurnal Puruhita, vol. 7, no. 2, hlm. 39–42, 2025, doi: 10.15294/puruhita.v7i2.37902.
Y. Tertibi, “The Urgency of Artificial Intelligence Regulation in Indonesia’s Criminal Justice,” METTA: Jurnal Ilmu Multidisiplin, vol. 5, no. 3, hlm. 1–18, 2025, doi: 10.37329/metta.v5i3.4501.
