Criminal Liability in Algorithmic Journalism: Critical Analysis of Generative AI and Automated News Content - Zainal Arifin; Emi Puasa Handayani; Souad Ezzerouali (Universitas Muhammadiyah Purwokerto, 2026)
This study aims to analyze the legal implications of the use of generative artificial intelligence (generative AI) in journalistic practice against three forms of press violations, namely defamation, the spread of misinformation/disinformation, and privacy violations, as well as to assess the extent of the readiness of the applicable legal framework in overcoming these problems. The development of generative AI in journalism presents new challenges in criminal law, especially regarding accountability for news content generated automatically without direct human intervention. This study uses a normative-doctrinal approach combined with a comparative analysis of the Indonesian criminal law framework and the latest international regulatory developments. The results of the study show that conventional criminal accountability principles, especially the elements of mens rea and actus reus, are not fully compatible with the characteristics of AI which is autonomous, thus causing legal gaps and regulatory uncertainty. To overcome these problems, this study offers three strategic solutions, namely: (1) modification of the corporate accountability model by expanding the definition of criminal offenders, (2) the application of limited strict liability (limited strict responsibility) for digital platforms, and (3) the establishment of a risk-based algorithmic supervision mechanism involving various stakeholders. This research makes a theoretical contribution to the development of criminal law in the digital era while offering practical recommendations for policymakers in formulating an AI accountability system that is adaptive, balanced, and supports modern journalism innovation.