Skip to content

The Direction of Indonesian Criminal Law Policy in Addressing Artificial Intelligence Disruption in the Digital Era

Oct 2026 · Journal of Law Politic and Humanities
Digitalization, Law, and Regulation

Abstract

The rapid advancement of Artificial Intelligence (AI) in the digital era has created significant disruptions that not only affect technological aspects but also challenge the classical constructs of Indonesian criminal law. Possessing analytical capabilities and decision-making autonomy without human psychological dimensions, AI presents normative dilemmas regarding the principle of culpability (mens rea) and legal certainty. This study aims to analyze the current legal construction of AI regulation within the Indonesian criminal law system and to formulate the future direction of an adaptive and equitable criminal law policy. Utilizing a normative legal research method, this study analyzes relevant statutes and legal policy literature. The findings reveal that current AI regulations in Indonesia remain partial, indirect, and fragmented (such as within the Electronic Information and Transactions Law), leading to normative gaps, difficulties in attributing criminal liability, algorithmic evidentiary challenges (the "black box" phenomenon), and elevated risks of AI-driven cybercrimes. The future direction of Indonesian criminal law policy must be anticipatory and progressive by establishing dedicated risk-based regulations, reconstructing criminal responsibility frameworks (including corporate and strict liability), strengthening preventive measures, enhancing law enforcement capacity, and harmonizing with international legal standards.

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.