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Reimagining Criminal Liability in the Age of Artificial Intelligence: Toward a Comparative and Reform-Oriented Legal Framework

Journal of Law and Legal Reform · Vol. 6 No. 4 (2025) · pp. 1805–1838

DOI
10.15294/jllr.v6i4.35540
ISSN
2715-0968, 2715-0941
Published
2025-10-30
Publisher
Faculty of Law, Universitas Negeri Semarang, Indonesia
Language
eng
License
creativecommons.org/licenses/by-nc-sa/4.0

Abstract

As artificial intelligence (AI) systems increasingly permeate decision-making processes across sectors—from autonomous vehicles to predictive algorithms in finance and law enforcement—traditional frameworks of criminal liability face unprecedented challenges. This article critically examines the adequacy of existing criminal law doctrines in attributing liability when harm arises from autonomous or semi-autonomous AI actions. It explores the tension between actus reus and mens rea in cases involving algorithmic behavior, and interrogates whether AI entities can or should be treated as legal subjects under penal law. Through a comparative legal analysis of jurisdictions including the United States, the European Union, Japan, and Indonesia, the study identifies divergent approaches to regulating AI-related harm and assigning culpability. The article highlights emerging models such as strict liability, vicarious liability, and hybrid regulatory frameworks, and evaluates their potential for adaptation within Indonesia’s evolving legal system. Special attention is given to the role of developers, corporations, and state actors in shaping accountability mechanisms. The paper concludes by proposing a normative framework for reimagining criminal liability in the age of AI—one that balances innovation with legal certainty, and integrates ethical safeguards, technological transparency, and procedural fairness. This framework aims to inform future legislative reform in Indonesia and contribute to global discourse on AI governance and criminal justice.

Keywords

Collaboration network of the authors

40 authors · 143 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ridwan ArifinAffiliation unknown12
Indah Sri UtariAffiliation unknown10
Anis WidyawatiAffiliation unknown9
Cahya WulandariState University of Semarang8
Rodiyah RodiyahAffiliation unknown8
Wahyu DwiantoAffiliation unknown5
Rohadhatul AisyAffiliation unknown5
Danang Sudarwoko AdiAffiliation unknown4
‪Eram Tunggul Pawenang‬State University of Semarang4
Nadhira Madania DimartiAffiliation unknown4
Rasdi RasdiAffiliation unknown4
Ubaidillah KamalAffiliation unknown4
Nizamuddin AliasAffiliation unknown4
Aprila NiravitaAffiliation unknown3
Ahmad Zaharuddin Sani Ahmad SabriAffiliation unknown3
Irma CahyaningtyasAffiliation unknown3
Ali MurtadhoAffiliation unknown3
Yusup AminAffiliation unknown3
Winarsih WinarsihState University of Semarang3
Rini FidiyaniAffiliation unknown3
Adrian HadiputraAffiliation unknown2
Ikram AmrullahAffiliation unknown2
Hibatullah MaajidAffiliation unknown2
Mutmainah Nur QoiriAffiliation unknown2
Destika Santi Putri SarfitriAffiliation unknown2
Jamaluddin Jamal MuhsenAffiliation unknown2
Windiahsari WindiahsariAffiliation unknown2
Salsabila TriandaniAffiliation unknown2
Rizqan NaelufarAffiliation unknown2
Roziya AbuAffiliation unknown2
Syatriawan LaguraAffiliation unknown2
Nadela JusticeaAffiliation unknown2
Juniza Indah SetiawatiAffiliation unknown2
Moh SamanAffiliation unknown2
Sonny Saptoajie WicaksonoAffiliation unknown2

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jllr/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Reimagining Criminal Liability in the Age of Artificial Intelligence: Toward a Comparative and Reform-Oriented Legal Framework</dc:title>
	<dc:creator xml:lang="en">Maskur, Muhammad Azil</dc:creator>
	<dc:creator xml:lang="en">Masyhar, Ali</dc:creator>
	<dc:creator xml:lang="en">Damayanti, Ratih</dc:creator>
	<dc:creator xml:lang="en">Ramada, Diandra Preludio</dc:creator>
	<dc:creator xml:lang="en">Sanyal, Subhra</dc:creator>
	<dc:subject xml:lang="en">Artificial Intelligence (AI)</dc:subject>
	<dc:subject xml:lang="en">Criminal Liability</dc:subject>
	<dc:subject xml:lang="en">Autonomous Systems</dc:subject>
	<dc:subject xml:lang="en">Comparative Criminal Law</dc:subject>
	<dc:subject xml:lang="en">Algorithmic Accountability</dc:subject>
	<dc:description xml:lang="en">As artificial intelligence (AI) systems increasingly permeate decision-making processes across sectors—from autonomous vehicles to predictive algorithms in finance and law enforcement—traditional frameworks of criminal liability face unprecedented challenges. This article critically examines the adequacy of existing criminal law doctrines in attributing liability when harm arises from autonomous or semi-autonomous AI actions. It explores the tension between actus reus and mens rea in cases involving algorithmic behavior, and interrogates whether AI entities can or should be treated as legal subjects under penal law. Through a comparative legal analysis of jurisdictions including the United States, the European Union, Japan, and Indonesia, the study identifies divergent approaches to regulating AI-related harm and assigning culpability. The article highlights emerging models such as strict liability, vicarious liability, and hybrid regulatory frameworks, and evaluates their potential for adaptation within Indonesia’s evolving legal system. Special attention is given to the role of developers, corporations, and state actors in shaping accountability mechanisms. The paper concludes by proposing a normative framework for reimagining criminal liability in the age of AI—one that balances innovation with legal certainty, and integrates ethical safeguards, technological transparency, and procedural fairness. This framework aims to inform future legislative reform in Indonesia and contribute to global discourse on AI governance and criminal justice.</dc:description>
	<dc:publisher xml:lang="en">Faculty of Law, Universitas Negeri Semarang, Indonesia</dc:publisher>
	<dc:date>2025-10-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/jllr/article/view/35540</dc:identifier>
	<dc:identifier>10.15294/jllr.v6i4.35540</dc:identifier>
	<dc:source xml:lang="en">Journal of Law and Legal Reform; Vol. 6 No. 4 (2025): October, 2025; 1805-1838</dc:source>
	<dc:source>2715-0968</dc:source>
	<dc:source>2715-0941</dc:source>
	<dc:source>10.15294/jllr.v6i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/jllr/article/view/35540/6565</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Muhammad Azil Maskur, Ali Masyhar, Ratih Damayanti, Diandra Preludio Ramada, Subhra Sanyal (Author)</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
</oai_dc:dc>