The Legal Implications of Artificial Intelligence-Generated Evidence in Criminal Proceedings

Eldin Rahardian; M. Arif Syahputra

doi:10.37899/mjdpp.v3i2.378

Authors

Eldin Rahardian Faculty of Law, Universitas Airlangga, Indonesia
M. Arif Syahputra Faculty of Law, Universitas Airlangga, Indonesia

DOI:

https://doi.org/10.37899/mjdpp.v3i2.378

Keywords:

Accountability, Algorithmic Transparency, Artificial Intelligence, Criminal Proceedings, Due Process

Abstract

This study examines the legal implications of artificial intelligence-generated evidence in criminal proceedings, focusing on evidentiary admissibility, due process protection, and regulatory accountability. Using a doctrinal legal research design combined with comparative legal analysis, the study analyzes legislation, judicial developments, policy documents, and academic literature concerning the use of AI-generated evidence across multiple jurisdictions. The findings reveal that existing evidentiary doctrines remain applicable but are increasingly challenged by algorithmic opacity, limited explainability, reliability concerns, and the emergence of synthetic media. The study further demonstrates that AI-generated evidence may affect defendants’ ability to challenge evidentiary claims, thereby creating potential risks to procedural fairness and fair trial rights. The novelty of this research lies in the development of an integrated normative framework that connects traditional evidentiary principles with contemporary AI governance standards. The study contributes to ongoing debates regarding the regulation of AI within criminal justice systems and provides practical recommendations for strengthening transparency, accountability, and procedural safeguards in the use of AI-generated evidence.

References

Alkhseilat, A., Al-Billeh, T., Albazi, M., & Ali, N. A. (2024). The authenticity of digital evidence in criminal courts: a comparative study. International Journal of Electronic Security and Digital Forensics, 16(6), 720-738.

Alyami, N. S. (2026). The admissibility of AI-generated evidence in criminal proceedings: A comparative analytical study in light of Qatari legislation. Scientific Culture, 12(1.1), 4838–4848. https://doi.org/10.5281/zenodo.19387654

Ashley, K. D. (2022). Artificial intelligence and legal analytics: New tools for law practice in the digital age. Cambridge University Press.

Bagaric, M., Svilar, J., Bull, M., Hunter, D., & Stobbs, N. (2022). The solution to the pervasive bias and discrimination in the criminal justice system: transparent and fair artificial intelligence. American Criminal Law Review, 59(1), 95-148.

Bampasika, E. (2021). Artificial intelligence as evidence in criminal trial. CEUR Workshop Proceedings, 133–138.

Berk, R. (2025). Artificial intelligence, predictive algorithms, and criminal justice decision-making. Criminal Justice Review, 50(1), 25–43.

Bo, M. (2026). Artificial Intelligence in the Prosecution of International Crimes. International Law Studies, 107(1), 1. http://dx.doi.org/10.2139/ssrn.5597734

Casey, A., & Niblett, A. (2023). The death of rules and standards. Indiana Law Journal, 98(2), 567–604.

Champod, C., & Vuille, J. (2011). Scientific evidence in Europe: Admissibility, evaluation and equality of arms. International Commentary on Evidence, 9(1), 1–38. https://doi.org/10.2202/1554-4567.1123

Citron, D. K. (2022). Artificial intelligence and procedural fairness in digital justice systems. Fordham Law Review, 90(6), 2477–2505. https://doi.org/10.18572/1812-3791-2026-3-56-60

Cofone, I. G. N. A. C. I. O., & Strandburg, K. (2026). Algorithmic opacity as a principal-agent problem. NYU Journal of Intellectual Property and Entertainment Law, 15(2). http://dx.doi.org/10.2139/ssrn.6249838

Costa, A. (2026). Algorithmic accountability and evidentiary reliability in criminal proceedings. Computer Law & Security Review, 52, 105901.

Dou, L., & Dou, X. (2025). Towards just AI: Challenges and solution framework for algorithmic discrimination in judicial system. International Journal of Digital Law and Governance, 2(1), 39-81. https://doi.org/10.1515/ijdlg-2024-0020

Eubanks, V. (2022). Automating inequality: How high-tech tools profile, police, and punish the poor (Updated ed.). St. Martin’s Press.

Ferguson, A. G. (2024). The rise of big data policing: Surveillance, race, and the future of law enforcement (2nd ed.). New York University Press.

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2023). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 29(3), 1–24. https://doi.org/10.1007/s11948-020-00213-5

Ford, J. E., Wing, D., & Hurt, M. (2026). Artificial V. Expert Intelligence: The Risk of Ethical, Constitutional, and Evidentiary Violations in Representing Artificial Intelligence as Linguistic Expertise in the Legal System. International Journal for the Semiotics of Law-Revue internationale de Sémiotique juridique, 1-31. https://doi.org/10.1007/s11196-026-10526-6

Garamvölgyi, B., Ligeti, K., Ondrejová, A., & von Galen, M. (2021). Admissibility of evidence in criminal proceedings in the European Union. Eucrim, 1, 45–58. https://doi.org/10.30709/eucrim-2020-016

Grossman, M., & Grimm, P. (2025). Judicial approaches to acknowledged and unacknowledged AI-generated evidence. Science and Technology Law Review, 26(2). https://doi.org/10.52214/stlr.v26i2.13890

Gunarto, G., Yusri, Y., & Kusriyah, S. (2023). Reconstruction of Evidence Regulations in Civil Jurisdiction Based on Justice Value. Scholars International Journal of Law, Crime and Justice, 6(08), 447-452. https://doi.org/10.36348/sijlcj.2023.v06i08.008

Harris, M. (2023). Blockchain-based evidence in courts: standards, reliability, and admissibility challenges. Legal Studies in Digital Age, 2(3), 49-63.

Kordzadeh, N., & Ghasemaghaei, M. (2022). Algorithmic bias: review, synthesis, and future research directions. European Journal of Information Systems, 31(3), 388-409. https://doi.org/10.1080/0960085X.2021.1927212

Langer, M., & König, C. J. (2023). Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management. Human Resource Management Review, 33(1), 100881. https://doi.org/10.1016/j.hrmr.2021.100881

Meghwal, T. (2025). Admissibility of AI-Generated Forensic Evidence: Legal Standards, Ethical Challenges, and Comparative Jurisprudential Analysis. Available at SSRN 5998017. http://dx.doi.org/10.2139/ssrn.5998017

Mukherjee, A., & Chang, H. H. (2026). Copyright Laundering Through the AI Ouroboros: Adapting the ‘Fruit of the Poisonous Tree’Doctrine to Recursive AI Training. Available at SSRN 5256625.

Ostavciuc, D. (2022). Ensuring the right to defense in the criminal process. Supremația Dreptului, (2), 45-60. https://doi.org/10.52388/2345-1971.2022.e2.05

Pasquale, F. (2023). The black box society: The secret algorithms that control money and information (Updated ed.). Harvard University Press.

Ruano‐Chamorro, C., Gurney, G. G., & Cinner, J. E. (2022). Advancing procedural justice in conservation. Conservation Letters, 15(3), e12861. https://doi.org/10.1111/conl.12861

Schreier, M. (2021). Qualitative content analysis in practice (2nd ed.). Sage Publications.

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2023). Fairness and abstraction in sociotechnical systems. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 6(1), 59–68. https://doi.org/10.1145/3287560.3287598

Seng, D., & Mason, S. (2021). Artificial intelligence and evidence. Singapore Academy of Law Journal, 33(1), 241–279.

Shevchuk, O. (2024). Artificial intelligence-generated evidence and criminal procedural safeguards. International Journal for the Semiotics of Law, 37(4), 1125–1147. https://doi.org/10.1017/cri.2025.10090

Smuha, N. A. (2024). The EU Artificial Intelligence Act and high-risk AI systems in criminal justice. European Law Review, 49(2), 145–168. http://dx.doi.org/10.2139/ssrn.4874852

Sokolova, M. (2023). The role and position of AI evidence in civil litigation. Medicine, Law & Society, 16(1), 75–92. https://doi.org/10.18690/mls.16.1.169-190.2023

Surden, H. (2024). Generative artificial intelligence and evidentiary authenticity. Harvard Journal of Law & Technology, 37(2), 301–337.

Terpstra, B. L., & van Wijck, P. W. (2023). The influence of police treatment and decision-making on perceptions of procedural justice: A field study. Journal of research in crime and delinquency, 60(3), 344-377. https://doi.org/10.1177/00224278211030968

The, H. D. (2026). Ensuring the consistency of the presumption of innocence in a transitional criminal procedure model–the case of Vietnam. Cogent Social Sciences, 12(1), 2665011. https://doi.org/10.1080/23311886.2026.2665011

Veale, M. (2025). Regulating machine evidence: Transparency and accountability in AI-assisted adjudication. Modern Law Review, 88(1), 44–71.

Veale, M., & Borgesius, F. Z. (2022). Demystifying the draft EU Artificial Intelligence Act. Computer Law Review International, 23(4), 97–112. https://doi.org/10.9785/cri-2021-220402

Wang, X., Wu, Y. C., & Ma, Z. (2024). Blockchain in the courtroom: exploring its evidentiary significance and procedural implications in US judicial processes. Frontiers in Blockchain, 7, 1306058. https://doi.org/10.3389/fbloc.2024.1306058

Yeung, K. (2024). Algorithmic regulation, procedural justice, and the future of public law. Oxford Journal of Legal Studies, 44(1), 55–81. http://dx.doi.org/10.2139/ssrn.3483693

Yin, R. K. (2023). Case study research and applications: Design and methods (7th ed.). Sage Publications.

Yong, G. (2024). Theories of Procedural Adjudication. In Criminal Procedural Trial in China: Procedure of Excluding Illegal Evidence as an Example (pp. 9-62). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-4935-5_2

Završnik, A. (2022). Criminal justice, artificial intelligence systems, and human rights. ERA Forum, 23(1), 57–74. https://doi.org/10.1007/s12027-020-00602-0

Zhang, H. (2025). The Evidentiary Value of AI-Generated Data: A Framework for Reliability and Admissibility. Education and Social Work, 2(2), 34-41. https://doi.org/10.63313/ESW.9074