> **来源：[研报客](https://pc.yanbaoke.cn)** # Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains WHITE PAPER JANUARY 2026 # Contents Foreword 3 Executive summary 4 1 The problem: The vicious cycle of forced labour and data fragmentation 5 1.1 Persistence amid progress: The enduring nature of forced labour 5 1.2 The structural roots of fragmentation: Data, incentives, trust and governance gaps 6 1.3 Breaking the vicious cycle 9 2 The solution: The Global Data Partnership Against Forced Labour as a new model for collective impact 10 2.1 A system-level response to a systemic challenge 10 2.2 The theory of change 11 2.3 Why federated data and agentic AI are game changers 13 2.4 Proof of Concept in Thailand 15 2.5 Stakeholder value and collective advantage 18 2.6 Summary of the solution 19 3 The future: From proof to global impact 20 3.1 Minimum viable product: Scaling beyond the Proof of Concept 20 3.2 Trust by design: Governance, risks and enabling conditions for scale 21 3.3 Building global momentum and vision for 2030 22 Conclusion 23 Contributors 24 Endnotes 26 # Disclaimer This document is published by the World Economic Forum as a contribution to a project, insight area or interaction. The findings, interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum but whose results do not necessarily represent the views of the World Economic Forum, nor the entirety of its Members, Partners or other stakeholders. © 2026 World Economic Forum. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, including photocopying and recording, or by any information storage and retrieval system. # Foreword Maroun Kairouz Managing Director, World Economic Forum John F. Schultz Executive Vice-President, Chief Operating and Legal Officer, Hewlett Packard Enterprise Forced labour remains one of the world's most persistent and systemic labour rights challenges embedded across global supply chains and societies. Governments, businesses, trade unions and civil society organizations have invested heavily in addressing it, yet progress has stalled because our collective response has not yet been systemic enough. Despite vast effort and regulation, the data remains fragmented, incentives misaligned and trust scarce. None of this is inevitable. A growing community of partners has begun working together under the Global Data Partnership Against Forced Labour to address this challenge through a new model of collaboration. The Partnership represents a trusted, precompetitive infrastructure for coordinated global action. It connects insights securely across public, private and civil society systems without requiring any stakeholder to surrender control of their data or sovereignty. Partners collaborate safely, linking existing systems through shared governance and privacy-preserving technologies rather than creating another central database. The Global Data Partnership Against Forced Labour seeks to build the conditions for trust, interoperability and shared accountability, demonstrating that secure data collaboration is both technically feasible and institutionally possible. At its core, the Partnership uses a federated data model and agentic AI and provides an intelligence layer that connects fragmented data without requiring it to be moved or centralized. This design makes collaboration technically feasible, ethically sound and rights-based by design. Trust is built through transparency, accountability and inclusion, ensuring that data serves prevention, remedy and accountability. This white paper presents the early findings and lessons from the work. It highlights the persistent barriers that make forced labour difficult to detect and measure, the innovative approaches being developed to connect data safely and effectively, and the opportunities to scale this model globally in the years ahead. The Thailand Proof of Concept (POC) indicates that this model can be implemented safely and effectively, and that participants achieve greater impact when working together rather than alone. Ending forced labour will require leadership, collaboration and courage equal to the scale of the challenge. As the Partnership advances towards its 2026 development phase, we invite all stakeholders to participate, learn and act. By connecting insights securely, applying shared intelligence and demonstrating measurable progress, we can make forced labour a preventable risk rather than an enduring reality. # Executive summary The Global Data Partnership Against Forced Labour uses federation and agentic AI to transform fragmented data into shared intelligence – driving coordinated, privacy-preserving global action against forced labour. Forced labour is a systemic global challenge that demands systemic action. Despite decades of reform, compliance initiatives, advocacy and corporate due diligence, nearly 28 million people remain trapped in coercive work, across sectors and borders. $^{1}$ The causes are well known, yet progress has stalled because the ecosystem itself is fragmented: data is siloed, incentives are misaligned and trust is in short supply. Governments, businesses and civil society each collect important information, but these datasets rarely connect. Worker-generated insights, while among the most immediate sources of evidence, are often the least integrated into broader systems. The result is a vicious cycle: limited visibility weakens accountability, weak accountability erodes trust and mistrust prevents collaboration. Understanding where and why exploitation occurs remains difficult because information is scattered, incentives to share are uneven and collaboration often carries risk. Without trusted mechanisms to connect and verify data across stakeholders, visibility stays partial and collective action limited. The Global Data Partnership Against Forced Labour was created to break this cycle. Launched in 2025, it provides a trusted, precompetitive infrastructure that enables governments, companies, international organizations and civil society groups to collaborate securely without transferring, centralizing or giving up the sovereignty of underlying data. Built on a federated model, the Partnership links existing systems through shared standards and governance protocols, allowing participants to generate collective intelligence while retaining control of their own data. Federation and agentic AI sit at the core of this architecture, enabling analysis where the data resides and linking signals from various sources, such as worker grievances, labour inspections, recruitment records and migration flows, to uncover risk patterns invisible to traditional traceability. Starting with a Proof of Concept (POC) in Thailand, the Partnership illustrates how federated systems can reveal actionable insights while maintaining data privacy and sovereignty, strengthening coordination among governments, businesses and civil society. This model builds a foundation for collective advantage, with each stakeholder benefiting from greater visibility, efficiency and accountability while the ecosystem as a whole becomes more capable of prevention: - Governments gain clearer visibility to target enforcement and design responsive policy. - Businesses reduce duplication and strengthen compliance while improving risk management, supply chain resilience and brand trust. As regulatory scrutiny, investor expectations and due-diligence obligations intensify, collaboration offers a practical path to meet standards more efficiently and credibly. Civil society and worker organizations amplify worker voice and shape systemic solutions. - Investors and donors access reliable data to assess impact and direct resources where they are most needed. The solution is scalable by design. Its federated architecture can expand across sectors, regions and institutions without centralizing authority or compromising sovereignty. As participation grows, each new dataset enhances analytical power; stronger insights increase incentives to collaborate; and broader engagement accelerates prevention, thus creating a virtuous cycle of shared intelligence and collaborative action. The next step is collective. Ending forced labour will demand leadership, collaboration and courage equal to the scale of the challenge. Through shared evidence, aligned incentives and responsible innovation, stakeholders can move from isolated initiatives to coordinated impact. By connecting insights securely and acting on shared intelligence, governments, businesses and civil society can make forced labour a preventable risk rather than an enduring reality. As the Partnership moves towards its 2026 development, all stakeholders are invited to participate, learn and act, connecting insights, applying shared intelligence and demonstrating measurable progress together. 1 # The problem: The vicious cycle of forced labour and data fragmentation Even as global efforts expand, forced labour persists amid fragmented data, misaligned incentives and disconnected systems of accountability. # 1.1 Persistence amid progress: The enduring nature of forced labour According to the most recent International Labour Organization (ILO) estimates, almost 28 million people are subjected to forced labour worldwide, across both formal and informal sectors.3 Despite decades of progress in policy, advances in local action, corporate due diligence and civil society advocacy, overall prevalence has not declined but has in fact increased, pointing to a deeper, system-level weakness in detecting, preventing and addressing forced labour. It persists not because its causes are unknown, but because the data, accountability and coordinated action remain disjointed. $^4$ BOX 1 # Understanding forced labour The ILO Forced Labour Convention, 1930 (No. 29) defines forced labour as "all work or service which is exacted from any person under the menace of any penalty and for which the person has not offered himself or herself voluntarily".<sup>5</sup> It is a severe form of labour exploitation that can occur in any sector, country or supply chain, from manufacturing and agriculture to construction, domestic work and the informal economy. Forced labour depends on two core elements: (1) a credible threat or actual penalty (which may include violence, withholding identity documents, retaining wages, charging orindebting workers through recruitment fees, threats of deportation or dismissal, or debt bondage); and (2) work performed without the person's free and informed consent, including where they cannot leave the job when they wish.[6] The term "modern slavery" is broader. It includes forced labour but also encompasses human trafficking and practices resembling slavery, such as forced marriage or the sale of children. According to ILO estimates from 2022, nearly 28 million people are trapped in forced labour worldwide, including about 17 million in private-sector supply chains. This represents an increase of around 2.7 million since 2016, underscoring the need to address root causes, close data visibility spots and to strengthen victim protection. Over the past two decades, companies, governments, international organizations and civil society groups have launched hundreds of programmes to tackle forced labour. Social audits, worker hotlines, traceability tools, social compliance programmes and national enforcement of labour laws have each acted as important mechanisms in the fight against forced labour.[10] Governments have complemented these efforts through trade measures, import bans, country ratings and research, all of which strengthen regulatory and market accountability. In the private sector, due-diligence systems, ethical recruitment initiatives and responsible sourcing programmes have matured rapidly among a growing number of companies; however, too many companies still do not meaningfully investigate forced labour prevalence in their supply chains. At the same time, modern slavery and supply chain laws in the United Kingdom, Australia and Canada, import restrictions in the US and European Union (EU), and due-diligence legislation in Germany, France, Norway and Switzerland are strong external drivers of change. $^{11,12}$ Legislative progress across East Asia, South-East Asia and the Gulf Cooperation Council has added further momentum, while hundreds of global brands invest in worker voice platforms and traceability technologies. $^{13,14}$ Each of these efforts generates vast amounts of data, on suppliers, recruitment, enforcement and worker conditions, but most of it remains locked within institutional boundaries or incompatible systems. These advances have built essential momentum in addressing forced labour, yet progress remains insufficient. Most systems still operate independently, limiting the ability to see risks across supply chains or act on them collectively. Even where strong commitment exists, the information and tools needed for coordinated, system-level action remain dispersed across stakeholders, who often do not (or, in some cases, cannot) collaborate openly due to commercial, political or reputational sensitivities. # BOX 2 What's already happening in business and policy - Corporate action: Many leading companies have embedded human rights due diligence, supplier codes of conduct and responsible recruitment policies across their global operations and supply chains. - Industry collaboration: Sector alliances (e.g. apparel, electronics, agriculture) are developing shared tools for audits, worker voice amplification and grievance management. Government frameworks: National laws and trade policies increasingly require supply chain transparency and forced labour risk reporting. - International cooperation: The ILO, International Organization for Migration (IOM) and other United Nations (UN) partners coordinate technical assistance, global estimates and standards alignment. Why it still matters: Despite these advances, most systems rely on disconnected data sources, creating duplication and information gaps. A trusted and interoperable model of collaboration is needed to connect existing efforts, transforming parallel initiatives into shared visibility. # 1.2 The structural roots of fragmentation: Data, incentives, trust and governance gaps Global supply chains connect millions of enterprises and hundreds of millions of workers, but data and information about labour conditions within them is uneven, siloed and often inaccessible. Governments collect inspection and migration data; businesses gather audit and supplier information; non-governmental organizations (NGOs) and trade unions document worker experiences and grievances. Global estimates, statistical guidelines, academic research, grievance mechanisms and digital platforms have expanded the overall data landscape. $^{15}$ Each source offers great value on its own, yet rarely interacts with others. $^{16}$ This fragmentation makes it difficult to consistently and reliably identify patterns of risk, target prevention efforts or measure progress effectively. $^{17}$ The result is an incomplete picture of the issue at hand and limited collective capacity to prevent it. Forced labour data today spans three broad universes (corporate, civil society and public-sector systems) as described by Tech Against Trafficking's Three Universes of Data. The challenge is not scarcity but fragmentation: connecting these universes securely is what the Partnership seeks to achieve. # Universe: Corporate sector Examples of data types: Social audit reports, supplier compliance data, recruitment agency records, grievance data Typical sources: Companies, auditors, supply chain platforms Relevance to impact: Offers operational visibility and compliance evidence # Universe: Civil society Examples of data types: Worker surveys, hotline data, union reports, NGO case files, survivor testimonies Typical sources: NGOs, unions, worker voice platforms Relevance to impact: Reveals lived experience, risk patterns and hidden coercion Universe: Public sector Examples of data types: Labour inspections, prosecutions, migration and border data, humanitarian assessments Typical sources: Government ministries, enforcement agencies Relevance to impact: Anchors prevalence data and supports policy design Source: Tech Against Trafficking. (2024). The current landscape of data sharing. Building an effective data ecosystem to address forced labor in global supply chains, pp. 24-32. This fragmentation is reinforced by deeper structural barriers that shape how data, incentives, trust and governance interact. Information about forced labour is abundant in some areas yet absent in others, especially in the "first mile" of production or informal work, where conditions remain largely invisible and under-documented. Each actor gathers data within their own mandate, using different tools, standards and incentives. As such, existing sources vary widely in quality and rarely align on standards or interoperability, while workers often under-report exploitation due to fear of retaliation or lack of access to reliable mechanisms.[18] It is worth noting that differences in collection methods, verification standards and reporting incentives can produce misleading or incomplete information. Some datasets may reflect commercial, political or methodological bias, underscoring the importance of validation and triangulation across multiple sources before drawing conclusions. At the same time, incentives to share or even collect data are, in many cases, weak. When the benefits of transparency are uncertain, the risks high or exposure uncomfortable, stakeholders hesitate to disclose information. Uneven incentives and limited capacity for disclosure allow information gaps to persist across the system, particularly where commercial confidentiality, legal mandates or limited technical infrastructure constrain engagement. Compounding these barriers are persistent deficits of trust and governance. Privacy, sovereignty and reputational concerns deter collaboration, while longstanding mistrust between sectors reinforces silos and limits data exchange, even where objectives align. No shared governance framework exists to align accountability or measure collective impact, and information tends to flow vertically within sectors rather than horizontally across them. # 1 Data and measurement challenge Information is collected in different formats, languages and levels of detail depending on the actors involved, leading to fragmentation and inconsistency. - Limited interoperability is caused by a lack of common data standards or taxonomies to link audit results, inspection findings and worker feedback. Stakeholders lack consensus on which indicators define progress or how to measure prevalence consistently, resulting in measurement uncertainty. Each actor sees only a fragment of the supply chain, limiting the ability to identify risks or verify conditions beyond their immediate reach. # 2 Incentive challenge - Sharing data can appear risky without clear collective benefit, creating hesitation to disclose information that might expose weaknesses or invite scrutiny. - Businesses face commercial confidentiality and reputational concerns; governments prioritize sovereignty and legal mandates; and many actors – including civil society organizations, small enterprises and local authorities – operate with limited resources or infrastructure for secure data management. - Privacy obligations and ethical considerations regarding sensitive worker or operational data further discourage open sharing. # 3 Trust challenge Privacy and security concerns limit collaboration. - Historical mistrust among sectors discourages open exchange. - Without assurance that data will be used responsibly and reciprocally, even well-intentioned actors stay siloed. # 4 Governance challenge - Existing initiatives lack an overarching framework to align accountability and measure collective impact. Information flows vertically within sectors but rarely horizontally across them. FIGURE 1 The vicious cycle of fragmentation # 1.3 | Breaking the vicious cycle Addressing forced labour requires a systemic response equal to a systemic challenge, one that connects incentives, technologies and governance into a coherent global framework. The structural barriers outlined above do not operate in isolation. Together, they reinforce one another to create a self-perpetuating cycle in which limited visibility constrains accountability, weak accountability dampens incentives and a lack of trust prevents collaboration. This dynamic explains why progress remains slow even as awareness and regulation increase: information grows, but intelligence does not. In practice, this vicious cycle manifests in three interlinked ways: 1 Fragmented visibility: Data from audits, inspections, regulators and supply chain information is often scattered across multiple organizations and stakeholders, making it challenging to combine and analyse it effectively to identify patterns of risk or root causes. 2 Duplicated effort: Stakeholders often assess the same limited set of workplaces or suppliers, rather than building on one another's findings to expand visibility to those currently unseen. In the absence of interoperable frameworks, the lack of aligned definitions and data sharing mechanisms limits reach and wastes resources, creating fatigue for both workers and employers. 3 Inhibited accountability: Without shared evidence, responsibility for remedy and prevention remains unclear and diffuse. Overcoming the current cycle of fragmentation is not merely a technical challenge but a moral, economic and governance imperative. Forced labour persists because visibility and accountability remain uneven across the system. Even as data collection, compliance and reporting proliferate, the absence of shared intelligence prevents these efforts from adding up to real prevention. Without mechanisms that allow multiple actors to generate shared insight from distributed data while retaining control over their own information, efforts to eliminate forced labour will continue to be reactive and fragmented. Investments in data collection and compliance generate value for individual actors but fail to accumulate into a collective capacity for prevention at a systemic level. As a result: - Workers remain vulnerable because warning signals are missed. - Businesses face rising regulatory pressure and reputational risk without better tools for collaboration. - Governments struggle to enforce labour laws and standards efficiently across complex supply chains. Civil society organizations operate with limited visibility into where their efforts are most needed. Breaking this cycle requires an approach that makes data work as a shared asset – trusted, secure and actionable for all stakeholders. Only by creating systems that unlock collective advantage can the global community move from isolated effort to sustained impact. This transformation is both urgent and achievable. Emerging data technologies, shared governance models and regulatory momentum create a window of opportunity to act collectively. Addressing forced labour requires a systemic response equal to a systemic challenge, one that connects incentives, technologies and governance into a coherent global framework. Section 2 introduces the Global Data Partnership Against Forced Labour, a practical model for transforming fragmented information into shared intelligence, coordinated action and measurable impact at scale. 2 # The solution: The Global Data Partnership Against Forced Labour as a new model for collective impact Federation and agentic AI connect siloed datasets securely, transforming fragmented supply chain information into shared intelligence that drives coordinated prevention and compliance efficiency. # 2.1 A system-level response to a systemic challenge The fragmentation described in Section 1 has left governments, businesses and civil society organizations working in isolation, each with partial visibility of the problem. The Global Data Partnership Against Forced Labour was launched at the World Economic Forum's Annual Meeting in January 2025 to change that reality through a practical, evidence-based model of collaboration. The Partnership provides a technical and governance infrastructure that allows participants to analyse and share insights securely while retraining control of their own data. Its purpose is not to replace existing initiatives but to connect and strengthen them, linking worker voice systems, audits, government inspections and other data sources into a coherent evidence base that strengthens prevention, enforcement and remedy. By enabling this federated approach, the Partnership turns existing information into collective intelligence, and collective intelligence into coordinated action. Within the Partnership, five broad categories of actor contribute to and benefit from data collaboration: Workers: Individuals and communities whose safety and empowerment depend on the insights that lead to better protection and remedy. They benefit when shared data translates into earlier intervention, safer recruitment and access to remedy. Data providers: Organizations that generate data on risk or prevalence, such as government labour inspections, corporate audits, grievance mechanisms, recruitment agency records, worker surveys and NGO case documentation. Their contributions build the evidence base that makes risk visible and drives collective accountability. Data users: Entities that apply insights for decision-making, such as companies and employers integrating risk data into due-diligence systems; government ministries targeting inspections based on trends; international organizations coordinating action; donors or investors directing resources towards prevention; and even workers themselves. These users rely on visibility to hire responsibly, meet buyer expectations and strengthen compliance across supply chains. Data stewards: Actors responsible for data quality, privacy and governance, including technology providers, national data authorities and academic or research institutions that validate analytical integrity. They ensure that data remains accurate, secure and ethically managed throughout the system. Intermediaries: Organizations that translate and connect data across systems, for example, federated infrastructure partners, multistakeholder industry platforms and civil society intermediaries that enable collaboration between sectors. They make interoperability possible, ensuring that insights flow across boundaries and reach those who can act on them. # 2.2 | The theory of change The Partnership's theory of change explains how better data collaboration can address the systemic barriers outlined in Section 1 of this paper: fragmented incentives, disconnected information systems and limited capacity for coordinated action. It brings together three interdependent dimensions – incentives, technology and impact – that together create the conditions for collective impact. # Incentive alignment Stakeholders face rising expectations for transparency and ethical conduct. The Partnership helps them meet these expectations more efficiently by sharing evidence rather than duplicating effort. - Governments gain clearer visibility to target enforcement and allocate inspection resources. - Businesses strengthen compliance with forced labour laws and trade requirements while improving the credibility of due-diligence reporting. Civil society organizations and unions amplify worker voice through integration of grievance and case-management data, strengthening advocacy and accountability. - Investors and donors obtain more reliable indicators of performance and measurable results. # Technology enablement - Advances in federated data systems, privacy-preserving technology and AI make collaboration on data technically feasible and legally compliant. - Queries are executed at the data's place of residence, with only aggregated, anonymized insights exchanged across the network. - This federated design removes the need for a central database while maintaining analytical power and traceability across multiple data owners. # Impact generation - When data sources connect safely, patterns of risk, prevalence and root causes become visible and actionable. - Collective intelligence supports earlier intervention, targeted remediation and more efficient use of resources. - The intended result is measurable improvement in worker protection, policy targeting and systemic accountability. Better data alone does not automatically lead to better decisions or outcomes. Impact depends on how information is interpreted, shared and acted upon by different stakeholders. # From visibility to change Better data alone does not automatically lead to better decisions or outcomes. Impact depends on how information is interpreted, shared and acted upon by different stakeholders. The Partnership therefore creates a platform for discovery, enabling participants to identify both where data generates value for their own efforts and where shared intelligence can unlock collective impact. This collaborative process provides the opportunity to develop impact pathways that link data to practical outcomes, such as improved risk targeting, more consistent remediation and stronger worker protection. Over time, these shared insights can inform responsible recruitment, investment priorities and policy design, allowing both individual organizations and the wider system to make better, more coordinated decisions. As shared visibility grows, incentives can begin to shift across the system. When credible information on risk and response is accessible to multiple actors, lagging performers face reputational and regulatory pressure to improve, while frontrunners gain recognition and reduced compliance burden. Governments and donors can direct enforcement or investment where it will have the greatest effect, and worker organizations can use validated insights to advocate for remedy. Comparable experience from other domains (such as public health, climate action and financial transparency) demonstrates how privacy-preserving data collaboration can raise performance standards without centralizing information.[19] By enabling a similar architecture for the forced labour data ecosystem, the Partnership aims to transform disconnected activity into a virtuous cycle of insight, collaboration and coordinated prevention over time. FIGURE 2 Alignment of incentives, impact and technology case for collective impact # 2.3 Why federated data and agentic AI are game changers Data sharing to address forced labour already takes place across sectors, but most efforts remain limited in scope and siloed by organization, geography or purpose. Confidentiality obligations, differing legal frameworks and a lack of trusted connective infrastructure restrict how far information can flow and how effectively it can be used. As a result, many stakeholders see only part of the overall picture. Federated data systems offer a way to connect these existing efforts safely. They enable information to be analysed where it resides, allowing participants to share insights without giving up control or sovereignty. Rather than creating another central database, the Partnership links existing ones through shared standards and secure protocols, turning isolated initiatives into a connected network. AI enhances this potential. Integrated throughout the federated system, it can identify patterns and relationships that individual datasets cannot reveal – linking, for example, worker grievances with inspection outcomes or migration flows – while protecting privacy through privacy-preserving computation. Together, these technologies make possible a form of collective intelligence at scale: a system where trust, accountability and analytical power reinforce one another. # BOX 6 Key drivers for effective data federation in the forced labour ecosystem - Regulations, investor expectations and supply chain due-diligence requirements are driving new demand for credible, connected data.[20,21] Governments and companies now share a mutual interest in transparency, accountability and risk reduction. - Recent cross-sector partnerships have shown that sharing data in a precompetitive space allows organizations to manage systemic risks collectively while maintaining commercial independence.[22] Connective infrastructure can scale these gains across industries and regions. - Privacy-preserving technologies and federated learning are now proven and practical, enabling collaboration without compromising confidentiality.[23] Advances in AI and multilingual analysis make it possible to extract reliable insight from complex, unstructured information while maintaining data protection and sovereignty.[24] - Computational capacity, cloud infrastructure and interoperability standards have matured, allowing distributed analytics at scale. International frameworks on data governance and responsible AI can provide guardrails for ethical adoption, creating conditions for innovation, measurable impact and shared accountability. Together, these advances align privacy, sovereignty and collaboration, demonstrating that secure data systems can deliver trust, efficiency and insight without centralizing information. Trust is built both technically, as each participant remains accountable for how they use their own and others' information, and institutionally, through clear mutual agreements that define the rules of participation and information sharing. The following sections outline how this architecture operates in practice, how it differs from traditional data sharing models and why advances in privacy, interoperability and multilingual AI make this a pivotal moment for collaboration against forced labour. $n =$ any number of participants Note: The diagram illustrates the overall generalized infrastructure and its space for application innovation, while also showing the scope through the three universes of data, the heterogeneous nature of data within each universe, the presence of both shared and sector-specific tools and services, and the core principle of federated data that connects them. Source: Hewlett Packard Enterprise # How it works - Federated technical architecture: Core applications and agreed protocols allow stakeholders to connect, share and collaborate on data or services without giving up autonomy or control. Instead of all or any data flowing into a single database, each participant maintains ownership of its data and systems while connecting through shared standards, interfaces and governance rules. - AI integration: AI functions as the intelligence layer of the federated network, connecting distributed datasets through shared standards and multilingual analysis. Rather than operating as a separate tool, AI is embedded throughout the system, linking information, identifying patterns and turning diverse data into shared insight while upholding privacy, transparency and accountability. - Retrieval-augmented generation (RAG): Enables large language models to generate more accurate and context-relevant responses by retrieving verified information from external knowledge sources before generating output. This approach ensures that responses reflect up-to-date and domain-specific context without requiring underlying data to be centralized or included in the model's original training set.[25] - Privacy-preserving technology: A suite of technologies enables participants to meet data protection and confidentiality requirements while collaborating safely. Examples include differential privacy, a form of anonymization, and federated learning, where AI models, not raw data, are shared to protect sensitive information. - Metadata standards: Global and regional frameworks provide a common understanding of insights across datasets. These standards improve data quality and interoperability and allow integration with AI and search technologies. - Multilingual agentic search: The architecture supports the interpretation of different languages while respecting context-specific terminology, resulting in increasing efficiency and accuracy. - Safety and governance: Governance will span the technology protocols, information management frameworks and safety. As requirements vary by region, local experts will contribute and adapt international templates to provide a trusted capability network. Each dataset within the federated system remains distinct in origin and governance. Analytical queries are designed to preserve this separation, applying weighting and quality control protocols so that no one dataset overrides another. Worker-generated data can be used to triangulate and validate system-level findings, ensuring that insights are grounded in lived experience while maintaining analytical balance. Each participant retains full ownership of their underlying data, algorithms and analytical models. The federated approach operates through query exchange, not data transfer, ensuring that proprietary intellectual property and commercial value remain fully protected. Commercially sensitive supplier information is also fully protected; the federated model enables every participating organization to keep control over what data is shared with whom. # 2.4 Proof of Concept in Thailand The Partnership's Proof of Concept (POC) focuses on Thailand to test how federated systems can connect diverse, anonymized datasets securely and generate signals that may support prevention and enforcement. It is developed collaboratively with participating organizations and in alignment with national priorities and data protection standards. # Why Thailand? Thailand's leadership on fair recruitment and migrant worker protection offers strong foundation for innovation.[26] The country is a key node in global supply chains, particularly in seafood, agriculture and manufacturing; it hosts millions of migrant workers and has developed a comprehensive set of labour laws, inspection systems and social dialogue mechanisms.[27,28,29] Multiple public and private actors collect information on employment conditions, worker complaints and migration flows. This work aligns with Thailand's commitment to ethical recruitment and responsible supply chain management, and it supports ongoing collaboration with the ILO, IOM and other international partners advancing decent work and digital transformation.[30] The POC serves as a demonstration of technical and institutional feasibility, not final impact. It shows that trusted data collaboration can function securely in real conditions and can serve as a foundation for future scaling. Insights emerging from this work can also help refine the Partnership's global design, informing how federated systems may operate responsibly across different contexts and with different stakeholders. # Testing data federation in Thailand At this stage, access to the platform is limited to participating members of the Global Data Partnership Against Forced Labour. Participation is expected to expand in upcoming phases, as technical validation, governance and capacity building progress. $n =$ any number of participants or datasets; RAG $=$ retrieval-augmented generation; LLMaaS $=$ large language model as a service Note: This figure illustrates the implementation of the POC. The upper section shows how diverse data sources are coupled with AI services to provide a functional interface for users. The lower section depicts the underlying federated architecture and execution steps that enable that workflow so that the system can generate a collective response without centralizing data and while preserving data sovereignty. Source: World Economic Forum and Hewlett Packard Enterprise # Scope and design - Categories of data being considered in the POC: Migration flow records, labour inspection results, prosecution data, government surveys, workplace assessments, supply chain data and anonymized worker grievance information from NGOs, trade unions and hotlines. - Safeguards: All datasets are anonymized and used under strict privacy-preserving protocols; no personally identifiable information is shared or transferred. Governance: Participating institutions maintain full control of their data while contributing aggregated outputs to a shared analytical layer. The Partnership's Technical Advisory Group (TAG) provides independent guidance on the design and safeguards of the POC. TAG includes representatives from the Partnership's technical, business and civil society communities, alongside experts in federated data infrastructure and ethical AI. Ethical assurance is also integral to the POC. To reinforce transparency and responsible innovation, Hewlett Packard Enterprise (HPE) has conducted an independent AI ethics assessment of the Global Data Partnership Against Forced Labour's design and implementation (see Box 7). HPE is committed to ethical, responsible AI. HPE's AI Ethics Working Group conducted an AI ethics assessment to align the POC with global standards and HPE's AI Ethics Principles: Privacy-enabled and Secure, Human-focused, Inclusive, Responsible and Robust. HPE's standard AI ethics assessment is particularly essential to this initiative given the sensitivity of data related to forced labour and its potential wide-ranging, global user base. The assessment follows a structured process, engaging a diverse group of specialists in technology, human rights, risk management and governance. Their role is to provide balanced oversight, challenge assumptions and offer recommendations for embedding ethics throughout the design and implementation. # Key recommendations and priority issues for the POC phase: Privacy-enabled: Enforce strict anonymization; avoid personal data; identify and remove personal data; embed privacy-by-design principles. Secure: Employ and encourage other partners to use robust encryption and data protection standards and techniques; establish strong partner organization and user authentication and verification; assign responsibility for auditing data inputs. Data remains in the domain of the data owner, who has exclusive control over usage and access. Human-focused: Restrict user access; facilitate a misuse workshop and adversarial testing before activating the solution beyond the POC; publish responsible use guidance for participants. Inclusive: Be open and inclusive to all regions and participants, their data and use cases; ensure workers or worker representatives trial the pilot and provide feedback on solution-design features; determine an approach for post-POC evaluation of bias risk against vulnerable groups; consider post-POC technical approaches that account for the needs of diverse workers, especially those with weak internet access, mobile-only connectivity or limited English proficiency. # Responsible: - Transparency: Build in transparency and explainability for how an AI outcome has been reached, identifying which sources contribute to which outputs. Accountability: Define responsibilities for all parties through robust governance; maintain continuous ethical oversight; implement logging and accountability measures. Robust: Conduct stress testing and quality assurance upfront and regularly; assign someone to take responsibility for ongoing checks and improvements; collect user feedback to improve tool accuracy. An additional ethics assessment is conducted as the POC transitions into a fully scaled, publicly accessible platform. # Approach to data protection All worker-level data is de-identified at the source, with metadata scrubbing to prevent indirect re-identification. Access controls and differential privacy techniques ensure that no analysis can be traced back to individual workplaces or persons. These safeguards are developed in consultation with data protection specialists and reviewed as the implementation advances. Analytical outputs are aggregated by default and not attributable to individual participants unless explicitly authorized, enabling shared learning and policy use while preserving control and consent. # 2.5 Stakeholder value and collective advantage The Thailand POC gives a first indication of how the Partnership can create value for a wide range of stakeholders by translating collaboration into practical benefits. Each actor stands to gain from shared intelligence – whether through stronger governance, improved efficiency, more effective advocacy or greater accountability across supply chains. # For governments - Improves visibility across migration and labour systems Supports targeted labour inspection, intervention and responsive policy design through shared intelligence - Strengthens trade relationships by improving transparency, advancing worker protection capability and reducing risk across international supply chains # For businesses Enhances forced labour due diligence by integrating worker voice data, enforcement records and cross-border migration patterns - Reduces duplication and compliance costs through secure integration of existing data streams - Enables more targeted and preventive action by helping companies identify patterns and areas of elevated risk # For civil society and worker organizations - Ensures that worker grievances and field-level insights inform systemic action Strengthen capacity for evidence-based advocacy and prevention # For investors and donors - Provides credible data to assess impact and allocate resources effectively # For international institutions and data platforms - Enables alignment of definitions and metrics without centralizing databases Through this structure, the Partnership can function as connective tissue that links multiple existing initiatives into a coherent system of accountability. It leverages, rather than replaces, prior investments and helps participants see how collaboration can generate both individual and collective value. Emerging use cases and impact pathways include those outlined in Table 1. TABLE 1 Examples of emerging use cases and impact pathways <table><tr><td>Use case</td><td>Potential value</td><td>Examples of data to explore federation</td></tr><tr><td>Protecting vulnerable populations and strengthening humanitarian response</td><td>Connecting humanitarian, migration and labour data to anticipate vulnerability and guide intervention</td><td>Labour inspection data, migrant registration records, humanitarian assessments</td></tr><tr><td>Building supply chain visibility and corporate accountability</td><td>Combining company, supplier and enforcement data to strengthen due diligence and reduce audit duplication</td><td>Social audit data, supplier questionnaires, recruitment agency data, grievance mechanisms</td></tr><tr><td>Elevating worker voice and embedding empowerment in solutions</td><td>Integrating worker voice and grievance data with independent datasets so worker perspectives shape response and remedy</td><td>Worker surveys, hotlines, union/association reports, digital worker voice apps</td></tr></table> # 2.6 Summary of the solution The Partnership shows that, when the right conditions for trust, shared value and leadership commitment are in place, collaboration on data can be both technically and institutionally feasible. By aligning incentives for collaboration, applying federated data infrastructure and agentic AI technologies and protecting privacy and sovereignty, the Partnership is beginning to turn fragmented information into shared intelligence. The POC in Thailand represents the first step towards realizing this vision, testing how secure data federation can operate in real world conditions and generate signals that inform prevention and enforcement. These early lessons can guide the refinement of governance, technical standards and collaboration models as the Partnership continues to evolve. In later phases, aggregated insights are expected to be made publicly available to support transparency, research and accountability, while maintaining confidentiality and national data sovereignty. Ultimately, the Partnership's goal is not only to generate insight but to create a platform for coordinated action, enabling participants to identify systemic risks, strengthen remediation and track progress collaboratively across supply chains. 3 # The future: From proof to global impact The Partnership's vision for 2030: a safeguarded ecosystem based on federated data and agentic AI, where connected intelligence makes forced labour a preventable global risk. The POC in Thailand establishes that secure collaboration on data is both technically and institutionally feasible. The next phase focuses on consolidating those lessons into a minimum viable product (MVP) that can operate sustainably, scale responsibly and attract broad participation across sectors and regions. # 3.1 Minimum viable product: Scaling beyond the Proof of Concept The MVP represents the next step in translating feasibility into functioning public infrastructure. It brings together the core elements tested during the POC – federated architecture, privacy-preserving analytics and multistakeholder governance – into a single operational model designed for scale. # BOX 8 Core components of the MVP # Technical foundations - A fully functional f