<p>Mapping of reviewed studies.</p>

IF-LT research clusters around online recruitment, recruitment fraud, illicit massage businesses (IMBs), while OA-LT examines supply chains, child labor, violence indicators on social media, modern slavery disclosures and high-risk sectors such as the fishing industry. Common approaches include case studies with explainable analytics, interpretable classifiers, topic modeling and advanced natural language processing (NLP) methods like sentiment analysis, transformers and large language models; positive-unlabeled learning, probabilistic modeling, and spatial analysis.

Despite attention to online LT, the review reveals a gap in quantitative applications of state-of-the-art ML to online content for LT detection. Interdisciplinary and transferable techniques developed for detecting online sex trafficking (ST) offer an adaptable pathway towards automation and law enforcement applications. Beyond methodological gaps, the review emphasizes responsible and context-aware AI, including weighted indicator modeling, remedies for class imbalance, multimodal data integration, label-efficient learning with expert annotations, and clear diagnostic reporting to support accountable deployment.</p></div>