Decoding legal processes: AI-driven system to streamline processing of the criminal records in Moroccan courts

Abstract

In Morocco, the manual process of feeding the criminal records database has become more challenging as the number of judgments has increased. This operation is carried out in two stages. The court clerk classifies the judgments as convictions or non-convictions, then extracts the guilty personal details and case information from those that present a conviction to feed the criminal records database. The current process has several drawbacks such as prolonged processing times, potential errors, and data confidentiality concerns. In this paper, we present a novel Arabic decision support legal system designed to assist in feeding the criminal records database. The system comprises two key components. The first component is a CNN-based judgment classifier that classifies judgments into convictions and non-convictions, while the second component is a legal entities extractor that can efficiently extract 11 entities from judgments classified as conviction. Both models were trained on purpose-built Arabic legal corpora created based on 4966 Arabic verdicts issued from the Moroccan courts. The judgment classifier achieves an accuracy of 96.6% on the judicial decision corpus, 98% on the Khaleej dataset, and 96.27% on the ECHR dataset. The legal entities extractor achieves 98.42%, 93.72%, and 93.5% F-scores on the legal entities corpus, the ANERCorp dataset, and the CONLL2003 respectively, outperforming prior research. These results highlight the potential of the system in improving the operation of feeding the criminal records database. Furthermore, the creation of these Arabic legal corpora provides valuable resources for enhancing legal document classification and domain-specific NER models in Arabic.