Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains

The report argues that forced labour persists due to a 'vicious cycle' of fragmented data, misaligned incentives, and lack of trust among stakeholders. To break this, the World Economic Forum introduces the Global Data Partnership Against Forced Labour, which utilizes a federated data model and agentic AI to generate collective intelligence without requiring stakeholders to centralize or surrender their data. A Proof of Concept (POC) in Thailand demonstrates the technical and institutional feasibility of this approach, linking diverse sources such as labour inspections, migration records, and worker grievances. The document outlines a transition toward a Minimum Viable Product (MVP) and a 2030 vision to make forced labour a preventable global risk through scalable sectoral and regional expansion.

• Forced labour remains a systemic global issue, with ILO estimates indicating that nearly 28 million people are subjected to it worldwide, including approximately 17 million within private-sector supply chains.
• Progress in eliminating forced labour has stalled because of a 'vicious cycle' where fragmented data, misaligned incentives, and a lack of trust prevent the collective action necessary for systemic change.
• The Global Data Partnership Against Forced Labour employs a federated data model and agentic AI to allow stakeholders to analyze and share insights securely while maintaining data sovereignty and ownership.
• The Partnership's technical architecture utilizes Retrieval-Augmented Generation (RAG) and privacy-preserving technologies, such as differential privacy and federated learning, to ensure accurate, context-relevant responses without centralizing raw data.
• A Proof of Concept (POC) implemented in Thailand tested the federation of diverse datasets, including migration flow records, labour inspection results, and anonymized worker grievance information from NGOs and trade unions.
• The Partnership identifies five key actor categories that contribute to and benefit from the ecosystem: workers, data providers, data users, data stewards, and intermediaries.
• The transition from the POC to a Minimum Viable Product (MVP) involves establishing standardized metadata, participation agreements, and a sustainable financing model to enable sectoral and regional scaling.
• The long-term vision for 2030 is to establish a safeguarded global ecosystem where connected intelligence makes forced labour a preventable risk, similar to current standards for financial transparency or climate reporting.

APA

World Economic Forum (2026). Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains. https://reports.weforum.org/docs/WEF_Harnessing_Data_and_Intelligence_for_Collective_Advantage_2026.pdf

Chicago

World Economic Forum. Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains. 2026. https://reports.weforum.org/docs/WEF_Harnessing_Data_and_Intelligence_for_Collective_Advantage_2026.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains |date=January 2026 |url=https://reports.weforum.org/docs/WEF_Harnessing_Data_and_Intelligence_for_Collective_Advantage_2026.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2026harnessing, author = {{World Economic Forum}}, title = {{Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains}}, institution = {World Economic Forum}, year = {2026}, month = jan, url = {https://reports.weforum.org/docs/WEF_Harnessing_Data_and_Intelligence_for_Collective_Advantage_2026.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }