用大模型自动检查企业反奴役声明，跨国家适用。
AIMSCheck: Leveraging LLMs for AI-Assisted Review of Modern Slavery Statements Across Jurisdictions
- 分三层评估企业反奴役声明合规性，提升可解释性。
- 在澳洲数据上训练的模型可在英加两国良好泛化。
- 公开数据集与工具，助力合规审查AI研究。
现代奴役法案要求企业披露其反奴役措施，以提高透明度并强化实践。然而，由于声明语言复杂多样、数量庞大，验证工作面临挑战。同时，因标注数据稀缺，开发NLP工具困难。随着多国推出相关立法，工具在不同司法管辖区的泛化能力亟待研究。为此，我们联合领域专家完成两项贡献：一是构建英国与加拿大的新标注数据集AIMS.uk和AIMS.ca，支持跨司法管辖区评估；二是提出端到端的合规验证框架AIMSCheck，将评估任务分解为三个层级，增强可解释性与实用性。实验表明，基于澳大利亚数据训练的模型在英加两国均表现良好，证明其具备广泛适用潜力。我们公开发布基准数据集与AIMSCheck，推动合规审查中AI技术的应用，并促进该领域进一步研究。
原文摘要 · Abstract (English)
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.
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