A New Dataset and Neural Benchmark for Multi-Label Classification of Modern Slavery Litigation

Modern slavery remains a critical global challenge, affecting millions of individuals through forced labor and human trafficking. As global supply chains become increasingly complex, corporations face growing legal, financial, and reputational risks related to human rights violations. Ensuring transparency and compliance with regulations such as the UK Modern Slavery Act 2015 has become essential, yet progress is hindered by the lack of any available, domain-specific datasets that enable automated monitoring of legal cases. To address this gap, we introduce the Modern Slavery Supply-Chain Cases (MOSSC) dataset, the first resource designed for multi-label text classification (MLTC) of modern slavery litigation. MOSSC contains 1,882 United Kingdom (UK) court judgments from 2015 to 2025, meticulously annotated with 329 fine-grained legal and factual labels relevant to human rights and supply chain compliance. We perform a comprehensive benchmarking study evaluating ten neural architectures, ranging from Bidirectional Gated Recurrent Units (BiGRU) to domain-adapted Bidirectional Encoder Representations from Transformers (BERT) models, including BERT-ECHR pre-trained on European Court of Human Rights cases. Our results demonstrate that BERT-ECHR achieves the highest performance, with a R-Precision at rank 2 (RP@2) of 0.8830 and a normalized Discounted Cumulative Gain at rank 2 (nDCG@2) of 0.8682. In addition, a lightweight BiGRU combined with Law2Vec embeddings offers an efficient alternative for resource-constrained applications. By providing MOSSC along with trained models and open-source code, we enable the development of artificial intelligence (AI)-driven tools for automated monitoring of supply chain risks. This approach empowers companies, auditors, and regulators to detect patterns of exploitation more effectively, assess legal exposure, and enhance compliance with environmental, social, and governance (ESG) requirements. Our work establishes the first robust baselines for AI-driven legal analytics in this domain, paving the way for greater transparency and ethical practices across global supply chains.