Can AI Make Our Supply Chains Fairer?
High-profile tragedies have exposed the urgent need for better worker protection in global supply chains. As new laws demand greater transparency, companies are turning to Artificial Intelligence (AI) for solutions. This page explores new academic research that asks: Is AI a true solution for social sustainability, or does it create new problems?
Figure 1: Research on this topic has accelerated, showing its growing importance.
Figure 2: Most studies discuss "General AI" rather than specific techniques.
The Two Faces of AI in the Workplace
The research reveals a fundamental duality: AI can be both a powerful tool for worker protection and a catalyst for new forms of exploitation.
AI as a Tool for Protection
AI excels at monitoring tangible, data-rich aspects of work. It can analyze real-time data to improve safety, detect risks of forced labor, and flag non-compliance much faster than human auditors.
- Enhancing Safety: Predictive analytics identify hazards before accidents happen.
- Detecting Forced Labor: Machine learning spots patterns indicating modern slavery.
- Screening for Child Labor: AI systems flag risks of underage workers.
AI as a Catalyst for Precarity
AI-driven automation and algorithmic management can erode worker rights, job security, and dignity. The focus on efficiency can create new pressures and forms of control.
- Undermining Job Security: Automation can displace workers or create insecure "gig" work.
- Suppressing Wages: "Deskilling" of tasks reduces workers' bargaining power.
- Algorithmic Control: Systems can push workers to extend hours and fire employees without due process.
AI's Impact on the 9 Core Labor Principles
The Ethical Trading Initiative (ETI) Base Code defines fundamental worker rights. The chart below shows which principles have been studied the most.
Click on a bar to see the specific findings for that principle.
A Framework for Responsible AI Adoption
To navigate AI's dual nature, the research proposes a three-stage framework to guide companies in deploying AI ethically and effectively. Use the controls below to explore the journey.
Assessing Readiness
Decision & Implementation
Continuous Improvement
Future Research Directions
The study identifies key areas for future investigation to build upon this foundational work and deepen our understanding of AI's role in social sustainability.
1. Deepen Empirical & Causal Specificity
Move beyond treating AI as a monolith. Future work should conduct longitudinal studies and use methods that can establish clear causal links between specific AI tools (e.g., NLP for grievance analysis) and their impact on specific labor rights during different phases of adoption.
2. Broaden Theoretical & Stakeholder Perspectives
Incorporate critical theories like Labour Process Theory to analyze how AI reconfigures managerial control and power. Research should also focus on the perspectives of suppliers in the Global South to understand how AI-driven transparency might reinforce existing power imbalances.
3. Advance Governance & Ethical Design
Shift towards creating and testing practical solutions. This includes designing ethical AI artifacts, developing inter-organizational governance models for data sharing (e.g., via blockchain), and establishing standards for model explainability and fairness-by-design to build stakeholder trust.