• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.149-155
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• 2022

The view of not fully denying the application of accomplice regulations to non-punishable opponents has fallen into a formal and logical circular argument that only provides formal grounds for non-punishment and has failed to provide practical grounds. In addition, it can be said that it has a criminal policy problem contrary to the legal sentiment of the general public by not punishing the active government travel activities of non-punishable accomplices. Therefore, in order to solve this problem, it is necessary to respect the legislator's intention that general non-punishment accomplices can be punished if they exceed the 'minimum government travel commission'. Therefore, if an unpunishable accomplice acts at least within the act required to realize the constituent requirements, the application of the accomplice regulations shall be excluded, and the accomplice regulations shall be applied only if they exceed that extent. In addition, if the indispensable counterparty is a protected person or has no responsibility (possibility of expectation), it can be said that it has provided a practical basis for the inability to punish, so it can be understood as impossible to punish. This interpretation method is thought to be able to present concrete validity in marginal cases where the counterparty is more responsible by substantially presenting the basis for an unpunishable accomplice.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.