AI-driven modern slavery detection for supply chain: A cross-jurisdictional legal text analysis

This study addresses a significant gap in Supply Chain Management (SCM) research by investigating the applicability of machine learning (ML) techniques, including state-of-the-art Large Language Models (LLMs), providing a foundational exploration into analyzing legal documents to identify supply chain-related narratives of modern slavery. We developed a dataset with 1714 court opinions from the USA and 436 legal dockets from Indian jurisdictions, meticulously annotated and curated in three global labels and thirteen factual labels. We benchmarked context-aware classifiers using traditional ML, deep learning (DL), and transfer learning, and also tested a zero-shot prompt-based model (Gemini 1.5-Flash). Various vectorization strategies and classifiers were compared for performance. Our findings reveal that the fine-tuned domain-specific BERT (LegalBERT/CASEHOLD) model achieved superior results, with an 89.55% F1-Score and 90.93% accuracy in identifying relevant cases. Also relevant, Gemini 1.5-Flash achieved comparable results (86.97% F1-Score, 86.1% accuracy), outperforming traditional ML/DL baselines. This work provides empirical evidence of how advanced analytical techniques can be leveraged for knowledge discovery to risk assessment by efficiently scanning large volumes of legal texts for relevant warnings. As one of the first studies to apply a context-aware approach to identifying modern slavery in supply chains through legal records—an under-explored area—this research make a significant contribution to discussions on improving supply chain modern slavery risk assessment and audit practices. • Advanced analytics technique in legal texts can be leveraged for knowledge discovery and can contribute to knowledge-based modern slavery risk assessment, audit and governance of supply chain. • State-of-the-art LLMs yielded superior performance, whereas inherently explainable models (e.g., Random Forest) achieved comparable results. • Fine-tuned large models achieve best performance over a prompt model in a experiment with 21 models, including classical machine learning algorithms. • A novel dataset and actionable recommendations can be explored to guide advanced research to address gap in analytics and Machine learning against modern slavery in supply chain management.