AIMS-QA: a framework for scaling the assessment of corporate modern slavery statements beyond compliance with AI-driven question answering

Abstract

Abstract The UK and Australian Modern Slavery Acts require large corporations to disclose annually how they address modern slavery risks in their operations and supply chains. Existing methods assess only a small proportion of these disclosures, or narrowly against explicit legal criteria, overlooking deeper indicators of corporate commitment. This paper presents AIMS-QA, an AI-driven framework for assessing modern slavery statements at scale and beyond compliance. It is built on a structured taxonomy synthesising 13 benchmarking methodologies from academic and civil-society analyses, consolidating 473 metrics into 209 streamlined questions across 115 thematic topics. From these, a 37-question priority framework captures the most widely used indicators while retaining key statutory requirements. The seven reporting criteria of the Australian Modern Slavery Act provide the analytic anchor, with each question tagged as either a statutory baseline requirement (reflecting jurisdiction-specific legal obligations) or a beyond compliance expectation (reflecting stakeholder, investor, and civil-society priorities). The paper then introduces a two-stage AI-driven question answering pipeline. The first stage uses a fine-tuned classification model to extract relevant evidence, which is passed to a large language model, guided by domain-specific prompts, to generate targeted answers. A second-stage retrieval-augmented generation (RAG) Watchdog intervenes where the classifier finds insufficient evidence or the language model cannot answer confidently. Designed for triage and screening rather than enforcement, with human-in-the-loop oversight, AIMS-QA gives policymakers, regulators, and stakeholders a research-informed means of strengthening the monitoring and impact of modern slavery legislation.