AI-enabled Legacy Data Integration with Privacy Protection: a Case Study on Regional Cloud Arbitration Court

Abstract

Abstract This paper reports an interesting case study on the Legacy Data Integration (LDI for short) for a Regional Cloud Arbitration Court. Due to the inconsistent structure and presentation, legacy arbitration cases can hardly integrate into the Cloud Court unless processed manually. In the case study, we aim to build an AI-enabled LDI method to replace the high-cost manual one and protect privacy during the process. Our method employs Optical Character Recognition (OCR), text classification, Named Entity Recognition (NER), and entity relation extraction to transform legacy data into system format. We train AI models to replace the tasks of the Court staff, such as reading and understanding legacy cases, removing privacy information, composing new records of cases to fit the Cloud Court, and inputting them through the system interfaces. With the applications of a Cloud Arbitration Court in Liaoning Provence, China, our intelligent LDI has similar effectiveness but greater efficiency than the manual LDI. Our method saves 90% of the workforce and achieves a 60%-70% information extraction rate of manual work. Our method achieves a comparable filtering effect for privacy while retaining the maximum amount of information. With the continuous development of informationalization and intelligentization in judgment and arbitration, many courts are building the court system using ABC technologies, namely Artificial intelligence, Big data, and Cloud computing. Our method could provide a practical reference when integrating legal data into the system.