Harnessing Data and Intelligence for Collective Advantage 2026

Key governance principles FIGURE 5 Transparency Participation criteria, data protection measures and analytical processes are documented and reviewable.Ethical use Data is used exclusively for prevention, remedy and accountability, not for punitive actions or commercial gain.Accountability Audit logs and impact metrics provide traceability for data queries and outputs.Inclusion Civil society organizations and worker groups contribute insights and data to ensure real world relevance and proportionality. Source: World Economic Forum Key questions in moving towards the MVP phase BOX 9 –How can ethical safeguards and data standards evolve as participation grows? –What mechanisms will ensure transparency and accountability in decision-making? –How can data from workers and civil society remain at the heart of the initiative as the model scales? –What governance design will balance innovation with rights protection? –How can consistent measures of impact be applied across jurisdictions? Scaling the Partnership is as much about mobilizing leadership as it is about technology. As the Partnership moves from POC to scale, its success will depend on broader participation and shared leadership. Governments, businesses, civil society and investors are invited to join the Partnership, connect their insights and act on shared evidence to accelerate prevention and accountability. By contributing securely to a trusted global system, participants strengthen not only collective visibility but also their own capacity to meet due-diligence, compliance and sustainability goals. The aim is to evolve the Global Data Partnership Against Forced Labour into an enduring global infrastructure for collective intelligence by 2030. In this vision, secure data collaboration becomes a standard feature of how the world monitors and prevents forced labour, much like financial transparency or climate reporting today.Key outcomes envisioned –Improved detection: Faster identification of systemic and emerging risks –Effective prevention: Resources targeted where data shows the greatest vulnerability –Shared accountability: Clearer, evidence- based pathways for remedy –Contribution to reduced forced labour prevalence: Making a significant, evidence- based contribution to lowering forced labour across sectors. Realizing this vision requires sustained collaboration, investment and leadership. The technologies now available make such transformation achievable within this decade.3.3 Building global momentum and vision for 2030 Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains 22