Leveraging Blockchain and Machine Learning to Promote Child Labor-Free Sustainable Development

Abstract

Child labor has been on the rise in recent years, which necessitates improved identification and reporting mechanisms. Current labor management systems, which are often manual or paper-based, lack traceability, audit, privacy, security, and trust features. This leads to challenges in detecting and reporting violations, particularly in large or remote areas. The persistence of this issue undermines the achievement of Sustainable Development Goals (SDGs) and highlights the important role of Corporate Social Responsibility (CSR) in addressing this challenge. Our paper proposes a solution combining machine learning and blockchain to automate child labor detection and ensure traceable, auditable, private, and secure reporting. Utilizing Decentralized Proxy Re-Encryption (DPRE), Zero-Knowledge Proofs (ZKPs), and oracles on the Ethereum blockchain, with decentralized storage, our approach maintains privacy and transparency. We present a child labor detection model using Mask2Former and ResNet-18 Convolutional Neural Network (CNN) to achieve high accuracy and reliability. The model’s performance is evaluated using various metrics, achieving an accuracy rate of 89.45%, a precision score of 0.906, and a recall score of 0.9332. Additionally, we assess smart contracts for cost-efficiency and security, and discuss the solution’s generalizability, challenges, and practical implications. We make the source code of our solution publicly available on GitHub.