Soppia: A Structured Prompting Framework for the Proportional Assessment of Non-Pecuniary Damages in Personal Injury Cases

TL;DR

Soppia is an AI-driven structured prompting framework that assists legal professionals in systematically assessing non-pecuniary damages in personal injury cases, ensuring comprehensive, consistent, and transparent evaluations aligned with legislative criteria.

Contribution

The paper introduces Soppia, a novel AI-based framework that operationalizes complex legal criteria into a transparent, replicable assessment tool for non-pecuniary damages in personal injury law.

Findings

Demonstrates Soppia's effectiveness in applying Brazilian legal criteria.

Enhances consistency and predictability in damage assessment.

Provides a versatile, explainable tool adaptable to various legal contexts.

Abstract

Applying complex legal rules characterized by multiple, heterogeneously weighted criteria presents a fundamental challenge in judicial decision-making, often hindering the consistent realization of legislative intent. This challenge is particularly evident in the quantification of non-pecuniary damages in personal injury cases. This paper introduces Soppia, a structured prompting framework designed to assist legal professionals in navigating this complexity. By leveraging advanced AI, the system ensures a comprehensive and balanced analysis of all stipulated criteria, fulfilling the legislator's intent that compensation be determined through a holistic assessment of each case. Using the twelve criteria for non-pecuniary damages established in the Brazilian CLT (Art. 223-G) as a case study, we demonstrate how Soppia (System for Ordered Proportional and Pondered Intelligent Assessment) operationalizes nuanced legal commands into a practical, replicable, and transparent methodology. The framework enhances consistency and predictability while providing a versatile and explainable tool adaptable across multi-criteria legal contexts, bridging normative interpretation and computational reasoning toward auditable legal AI.