Leveraging AI in the Kenyan Judiciary: A Case for Utilizing Text Classification Models for Data Completeness in Case Law Metadata in Kenya’s Employment and Labor Relations Court

Abstract

AI has been revolutionary in improving different professional fields and sectors. In the legal sector, AI is utilized, in a number jurisdictions, for different purposes both at the bar and bench level. The study investigates the efficacy of an AI algorithm in completing missing data in digitized documents, i.e. how AI can be utilized to achieve data completeness of precedents in the judiciary through text classification in order to achieve an optimal foundational basis for the creation of data sets that will facilitate the utilization of AI for different purposes. The Employment and Labor Relations court is used as a case study. The study analyzed the efficacy of 5 text classifier models: passive aggressive, linear regression, decision tree, random forest, and support vector machine (SVM) model. The results obtained from the study show that text classification can be automated successfully using machine learning techniques to generate case metadata. The accuracy of the text classifier methods utilized in the study range between 82% and 98%. Despite the data limitations faced in this study, the good results recorded help increase confidence that advanced NLP techniques have matured enough to be applicable to legal text in the Kenyan Judiciary. Findings from the study suggest that the success rates of the text classifier techniques are not merely dependent on text content, but the context of this content is also a determining factor – the nature of the cases and the structure of the legal system play an important role in the performance of text classifier models.