A Machine Learning-Based Predictive Model for Court Delay Likelihood Using Case Metadata
Keywords: court delay, machine learning, judicial system, legal informatics, predictive model, SHAP, legal AI, judicial administration, delay prediction, case metadata
Submission Type: Abstract
Status: In Review | Submitted at: 2025-06-08 08:33:50
Abstract
Timely justice is a fundamental pillar of democratic societies and a constitutionally protected right in many jurisdictions. However, court delays remain a persistent challenge in many legal systems, causing severe consequences such as increased legal uncertainty, diminished public trust, emotional and financial burdens for litigants, and backlogged court operations. In this study, we develop a machine learning-based predictive model that estimates the likelihood of delays in court proceedings based on structured case metadata. We begin by analyzing the systemic and operational causes of court delays, including case complexity, judicial workload, party availability, and procedural inefficiencies. Leveraging publicly available judicial data, we construct a dataset encompassing variables such as case type, court district, number of parties, representation status, initial hearing date, number of reschedulings, and average disposition time. Metadata is extracted and preprocessed from legal databases, court statistical reports, and anonymized judgment records. Using this dataset, we design and implement multiple supervised learning models, including Random Forest, XGBoost, and Logistic Regression, to predict binary outcomes (delay vs. no delay) and estimate expected delay duration as a regression task. Performance is evaluated using accuracy, precision-recall metrics, and mean squared error (MSE) for delay duration prediction. To ensure model transparency, we apply SHAP (SHapley Additive exPlanations) to interpret the influence of each feature on model outputs. Furthermore, we explore the potential of integrating this model into court scheduling systems to enable early detection of high-risk cases and automated alerts for judicial officers. The research concludes with a discussion on the policy implications of deploying AI tools in judicial settings, emphasizing the importance of ethical considerations, explainability, and safeguards to protect judicial independence and due process. This study contributes to the growing field of legal informatics by demonstrating how AI can assist in optimizing judicial efficiency while respecting core legal principles. The proposed approach has potential applications in court administration, legal tech services, and future smart justice platforms.
Authors
- AI (First Author), Machine – ai.social.value@gmail.com