Abstract:In 1999, major changes to Japan's criminal justice system were proposed, and over the next 10 years, many were implemented. One of the changes created the lay judge system (saiban-in seido), wherein citizens serve as fact finders during trials of serious criminal cases. The purpose of the lay judge is to enhance public trust in the judiciary while improving the quality of justice through the common sense of the average person. This article reviews how this major change to Japan's court system was implemented, … Show more
“…In the 1990s, proposals were made for significant changes to Japan’s criminal justice system, one of which created the lay judge system. The lay judge system aimed to increase public trust ( Reichel & Suzuki, 2015 ). A 2016 Japanese survey showed a 75.6% trust rate in judges.…”
People unfamiliar with the law may not know what kind of behavior is considered criminal behavior or the lengths of sentences tied to those behaviors. This study used criminal judgments from the district court in Taiwan to predict the type of crime and sentence length that would be determined. This study pioneers using Taiwanese criminal judgments as a dataset and proposes improvements based on Bidirectional Encoder Representations from Transformers (BERT). This study is divided into two parts: criminal charges prediction and sentence prediction. Injury and public endangerment judgments were used as training data to predict sentences. This study also proposes an effective solution to BERT’s 512-token limit. The results show that using the BERT model to train Taiwanese criminal judgments is feasible. Accuracy reached 98.95% in predicting criminal charges and 72.37% in predicting the sentence in injury trials, and 80.93% in predicting the sentence in public endangerment trials.
“…In the 1990s, proposals were made for significant changes to Japan’s criminal justice system, one of which created the lay judge system. The lay judge system aimed to increase public trust ( Reichel & Suzuki, 2015 ). A 2016 Japanese survey showed a 75.6% trust rate in judges.…”
People unfamiliar with the law may not know what kind of behavior is considered criminal behavior or the lengths of sentences tied to those behaviors. This study used criminal judgments from the district court in Taiwan to predict the type of crime and sentence length that would be determined. This study pioneers using Taiwanese criminal judgments as a dataset and proposes improvements based on Bidirectional Encoder Representations from Transformers (BERT). This study is divided into two parts: criminal charges prediction and sentence prediction. Injury and public endangerment judgments were used as training data to predict sentences. This study also proposes an effective solution to BERT’s 512-token limit. The results show that using the BERT model to train Taiwanese criminal judgments is feasible. Accuracy reached 98.95% in predicting criminal charges and 72.37% in predicting the sentence in injury trials, and 80.93% in predicting the sentence in public endangerment trials.
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