Modern slavery in global supply chains remains a critical concern, prompting governments in countries such as the UK, Australia, and Canada to introduce corporate transparency legislation, including Modern Slavery Acts. However, the growing volume of compliance reports has made manual analysis increasingly impractical. This challenge is compounded by the fact that many government agencies and NGOs responsible for assessing these statements operate in resource-constrained environments, limiting their ability to deploy large-scale language models. To address both issues, we present a novel AI framework for cross-jurisdictional compliance analysis. Our approach integrates four specialised teacher models, each trained using contrastive learning, prompt engineering, pseudo-labelling, and context-enhanced learning, which are distilled into a single lightweight, computationally efficient student model. This design enhances generalisability across legal frameworks while remaining affordable to deploy in low-resource settings. Experimental evaluations demonstrate that our method achieves higher accuracy and efficiency than existing approaches, offering a scalable, accessible solution for regulators and other stakeholders.

Modern Slavery Acts mandate that corporations disclose their efforts to combat modern slavery, aiming to enhance transparency and strengthen practices for its eradication. However, verifying these statements remains challenging due to their complex, diversified language and the sheer number of statements that must be reviewed. The development of NLP tools to assist in this task is also difficult due to a scarcity of annotated data. Furthermore, as modern slavery transparency legislation has been introduced in several countries, the generalizability of such tools across legal jurisdictions must be studied. To address these challenges, we work with domain experts to make two key contributions. First, we present AIMS.uk and AIMS.ca, newly annotated datasets from the UK and Canada to enable cross-jurisdictional evaluation. Second, we introduce AIMSCheck, an end-to-end framework for compliance validation. AIMSCheck decomposes the compliance assessment task into three levels, enhancing interpretability and practical applicability. Our experiments show that models trained on an Australian dataset generalize well across UK and Canadian jurisdictions, demonstrating the potential for broader application in compliance monitoring. We release the benchmark datasets and AIMSCheck to the public to advance AI-adoption in compliance assessment and drive further research in this field.

Despite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, the effectiveness of government oversight remains hampered by the challenge of scrutinizing thousands of statements annually. While Large Language Models (LLMs) can be considered a well established solution for the automatic analysis and summarization of documents, recognizing concrete modern slavery countermeasures taken by companies and differentiating those from vague claims remains a challenging task. To help evaluate and fine-tune LLMs for the assessment of corporate statements, we introduce a dataset composed of 5,731 modern slavery statements taken from the Australian Modern Slavery Register and annotated at the sentence level. This paper details the construction steps for the dataset that include the careful design of annotation specifications, the selection and preprocessing of statements, and the creation of high-quality annotation subsets for effective model evaluations. To demonstrate our dataset's utility, we propose a machine learning methodology for the detection of sentences relevant to mandatory reporting requirements set by the Australian Modern Slavery Act. We then follow this methodology to benchmark modern language models under zero-shot and supervised learning settings.