An Anticipatory Framework for Categorizing Nigerian Supreme Court Rulings

Abstract

It is important to recognize that a well-run judicial system contributes to the formation of a favorable atmosphere that fosters national growth. The efficient administration of justice is just as important to the court's efficacy as its capacity to be impartial, firm, and fair at all times. Notwithstanding these vital functions of the court, Nigeria's legal system is sometimes unsatisfactory and sluggish moving. People no longer trust the courts because of this, as most people think that justice postponed is justice denied. In recent times, machine learning methods have been used for predictive purposes in several domains. In this work, the authors used 5585 records of precedent rulings from the Supreme Court of Nigeria (SCN) between 1962 and 2022 to construct a prediction model for the categorization of judgments. Primsol Law Pavilion, an independently owned data repository, provided the data that was gathered from. Following data annotation and feature extraction, three classification methods (Decision Tree, Multi-layer Perceptron, and kNN) were used to construct the model. These techniques allowed for the identification of factors that significantly influence assessments, both from the literature and domain experts. The authors also looked at how two different feature extraction strategies, one based on correlation and the other on information, affected the models; the latter proved to be more successful in identifying pertinent characteristics. According to the study's findings, decision trees are the best machine learning algorithm for predicting how appeal cases that are submitted before the SCN would turn out.