AI-Enabled Forensic Risk Assessment: TRAP-18 System Architecture and Proof of Concept

Abstract

This study explores the development and validation of an AI-enabled forensic risk assessment system using the Terrorist Radicalization Assessment Protocol-18 (TRAP-18) as a proof of concept. Building on prior work demonstrating large language models’ (LLMs) ability to code TRAP-18 indicators with expert-level reliability, this project integrates structured professional judgment (SPJ) methodology with advanced AI frameworks, including LangChain and LangGraph. A prototype system was constructed to simulate a full TRAP-18 evaluation, encompassing indicator coding, justification generation, Bayesian probability estimation, and narrative risk formulation. Results demonstrate high consistency with human raters on proximal warning behaviors and strong agreement across multi-model workflows. The architecture emphasizes transparency, reproducibility, and bias mitigation, highlighting AI’s potential to augment forensic practice through structured reasoning, hypothesis testing, and scalable data integration. Beyond TRAP-18, this framework offers a pathway toward AI-assisted applications in general violence risk assessment, reinforcing human-AI collaboration in forensic psychology.