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.
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.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
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.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
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.
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.
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.· arXiv.org· 121 citations· ⚡9