Natural Language Processing-Based Model for Litigation Outcome Prediction: Decision-Making Support for Residential Building Defect Alternative Dispute Resolution

Abstract

Defects occurring during the maintenance phase of residential buildings not only undermine the quality of life of residents but also lead to disputes with contractors, which often escalate into litigation rather than being resolved through alternative dispute resolution (ADR), thereby increasing social and economic burdens. While previous studies have mainly focused on identifying the causes of defects, developing classification systems, and improving institutional frameworks, few have sought to predict litigation outcomes from precedent data to support decision-making during pre-litigation dispute resolution. This paper proposes a natural language processing-based multimodal and multitask prediction model that learns from precedent data using information available prior to litigation, such as the claims and evidence of plaintiffs and defendants and the claimed amounts. The proposed model simultaneously predicts judgment outcomes and grant ratios in defect-related disputes and can help to enhance the persuasiveness and voluntariness of ADR by informing parties about the likelihood of settlement and the potential risks of litigation. Furthermore, this paper proposes a decision-support framework for rational and evidence-based dispute resolution which can reduce stakeholder uncertainty and ultimately lower the frequency of litigation related to residential building defects.