An ensemble deep learning judgement prediction model for civil cases in Kenya.

Abstract

Abstract This study develops and evaluates an ensemble deep learning model combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an Attention Mechanism (AM) to predict judgments in Kenyan civil cases. With Kenya's judiciary facing a backlog exceeding 400,000 cases, this research addresses critical efficiency and consistency challenges. The CNN+BiLSTM+AM architecture extracts key textual features from legal documents, captures sequential dependencies in legal arguments, and prioritizes relevant information through attention weighting, providing both accurate predictions and interpretable results. Using stratified sampling across court levels, the study analyzes civil cases to identify influential predictors of judicial outcomes, including legal representation disparities, citation patterns, and procedural factors. Results demonstrate the model's superior performance compared to baseline approaches, with implications for case management, resource allocation, and access to justice. By providing data-driven insights into judicial decision-making, this research contributes to addressing systemic inefficiencies in Kenya's legal system while establishing a methodological framework applicable across similar jurisdictions. The findings support Kenya's judicial reform efforts by offering an innovative, technologically-driven approach to enhancing transparency, consistency, and efficiency in civil litigation.