In the complex world of global supply chains, the journey from raw materials to the finished product often spans multiple continents and countless suppliers. This global network facilitates efficiency but also conceals a troubling reality: modern slavery. An estimated 40 million people are victims of forced labor worldwide, affecting industries from fashion and agriculture to electronics. While artificial intelligence (AI) — especially large language models (LLMs) — promises new capacities for handling unstructured data and detecting hidden patterns, can it really shed light on this shadowy issue?
Uncovering the Dark Side of Modern Slavery in Supply Chains
Modern slavery doesn’t just occur in the visible parts of the supply chain; it thrives in its least regulated, most opaque layers. Companies may deploy audits and enforce compliance policies, but these measures often fail to capture abuses that happen far from oversight. Here, AI — and particularly LLMs, which can process large amounts of unstructured data from social media, local news, and text-based documents — offers a new potential solution for tracking labor practices at every level of the supply chain.
Project AIMS: Using AI to Analyze Modern Slavery Disclosures
Montreal, Mila – Quebec AI Institute has spearheaded Project AIMS (Artificial Intelligence Against Modern Slavery), which tackles a specific challenge: analyzing corporate modern slavery disclosures required under UK and Australian regulations. AIMS’ AI model assesses these reports, identifying gaps, vague language, and ambiguities. LLMs play a unique role here, using natural language processing to extract key information from unstructured documents, making it possible to evaluate a company’s commitment to addressing slavery risks systematically.
However, as Maha Khan from the United Nations University emphasizes, these tools need high-quality data and a strict ethical framework. Without this foundation, AI cannot go beyond surface-level assessment and may miss the nuanced realities of exploitation practices.
Real-World Examples: When AI and Unstructured Data Combine for Transparency
Some companies are already using AI to improve transparency in their supply chains and address Environmental, Social, and Governance (ESG) requirements, with a focus on modern slavery:
Exiger: Through its AI-powered risk assessment platform, Exiger helps companies spot slavery risks. By analyzing unstructured data, including public records and social media, the tool flags warning signs of exploitation.
Everstream Analytics: Known for its predictive analytics, Everstream uses AI to assess supply chain risks, including forced labor. Its real-time data capabilities allow companies to respond faster to disruption and poor practices.
Resilinc: Resilinc’s EventWatch AI tool monitors supply chain risks through unstructured data sources like news and social media. By providing early signals of exploitation in vulnerable regions, it enables proactive risk management.
Project AIMS (Mila): Project AIMS focuses on analyzing corporate compliance reports, using LLMs to detect gaps in transparency and encourage clearer reporting.
Ethical and Operational Challenges: Can AI Truly “See” Modern Slavery?
AI’s effectiveness in identifying slavery depends on the quality of data it receives, yet supply chains vary widely in data consistency. In low-regulation zones, data access may be limited or even absent. While LLMs can work with disparate data types, they still face the challenge of bias: models trained on partial or flawed data may produce skewed insights or miss crucial information.
Beyond Technology: Towards Broader Transformation
For AI to truly shine a light on supply chains, shared data standards and strong partnerships will be essential. Collaboration among companies, regulators, and tech innovators is vital to developing tools that are both effective and ethical. Project AIMS exemplifies this collaborative approach, seeking partnerships with NGOs and the private sector to refine its tools.
A Powerful Tool, but a Long Road Ahead
AI, and particularly language models, offers unprecedented potential to understand and expose hidden areas of the supply chain. However, this technology is only one part of a larger solution: meaningful change requires company commitments, evolving standards, and cross-sector collaboration to achieve real transparency and ethical practices. As it stands, AI can play a supporting role in identifying hidden risks, but eliminating modern slavery will demand a commitment that goes beyond algorithms.
Some interesting readings
These sources will provide additional context
International Labour Organization (ILO) - Global Estimates on Modern Slavery
Walk Free Foundation - Global Slavery Index
Mila – Quebec AI Institute, Project AIMS (Artificial Intelligence Against Modern Slavery)
United Nations University Centre for Policy Research - AI and Modern Slavery
Exiger and Slave-Free Alliance Partnership
Everstream Analytics - Predictive Analytics in Supply Chains
Resilinc’s EventWatch AI Platform
General Knowledge on Ethical AI Practices - AI Ethics and Data Quality
Best Practices for Industry Partnerships and ESG
General Information on ESG and AI in Modern Slavery
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