International Criminal Law Protection of Environmental Rights and Sentencing Based on Artificial Intelligence

Wang, Heyong; Sun, Ning; Wang, Hongwei; Tatarinov, Danila Vladimirovich

doi:10.1155/2022/4064135

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Ye et al [15] proposed a sequence-to-sequence (Seq2Seq) model under labeling conditions to provide a courtroom view to assist in sentencing based on factual descriptions of criminal cases with coding of charge labels. Wu et al [16] study the sentencing of environmental rights cases from the perspective of international criminal law and uses convolutional neural networks (CNNs) to determine the sentencing of environmental rights cases. Their results show that the introduction of CNNs improves the effect of the sentencing term prediction model and the fine prediction model significantly.…”

Section: Related Workmentioning

confidence: 99%

A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning

et al. 2022

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Sentencing prediction is an important direction of artificial intelligence applied to the judicial field. The purpose is to predict the trial sentence for the case based on the description of the case in the adjudication documents. Traditional methods mainly use neural networks exclusively, which are trained on a large amount of data to encode textual information and then directly regress or classify out the sentence. This shows that machine learning methods are effective, but are extremely dependent on the amount of data. We found that there is still external knowledge such as laws and regulations that are not used. Moreover, the prediction of sentences in these methods does not fit well with the trial process. Thus, we propose a sentence prediction method that incorporates trial logic based on abductive learning, called SPITL. The logic of the trial is reflected in two aspects: one is that the process of sentence prediction is more in line with the logic of the trial, and the other is that external knowledge, such as legal texts, is utilized in the process of sentence prediction. Specifically, we establish a legal knowledge base for the characteristics of theft cases, translating relevant laws and legal interpretations into first-order logic. At the same time, we designed the process of sentence prediction according to the trial process by dividing it into key circumstance element identification and sentence calculation. We fused the legal knowledge base as weakly supervised information into a neural network through the combination of logical inference and machine learning. Furthermore, a sentencing calculation method that is more consistent with the sentencing rules is proposed with reference to the Sentencing Guidelines. Under the condition of the same training data, the effect of this model in the experiment of responding to the legal documents of theft cases was improved compared with state-of-the-art models without domain knowledge. The results are not only more accurate as a sentencing aid in the judicial trial process, but also more explanatory.

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Section: Related Workmentioning

confidence: 99%