Supporting child labour free supply chains

Child labour continues to affect global supply chains, particularly where labour monitoring relies on manual, fragmented, or paper-based systems. These limitations reduce transparency and make it difficult for organisations to identify and respond to labour violations in large or remote production environments.

Through the work of Associate Professor Ibrar Yaqoob, Charles Sturt supported the development and evaluation of a framework that integrates automated child labour detection with secure, privacy-preserving reporting mechanisms. The research demonstrated how machine learning models can identify potential labour risks, while blockchain technologies enable traceable, auditable, and trustworthy record keeping across supply chains.

The study reported strong detection performance and demonstrated the technical feasibility of combining decentralised technologies with artificial intelligence to support ethical labour practices. This research contributes to efforts to promote safe, transparent, and accountable working conditions by strengthening the systems used to prevent child labour in global supply chains.