Regresar

Decentralized Systems and AI: Exploring the Intersection of Web3, Blockchain, and Predictive Machine Learning

Abstract:

This paper explores the integration of decentralized systems, Web3, and predictive machine learning in a case study for the development of a job recruitment platform. By leveraging the Internet Computer Protocol (ICP), the platform ensures secure, transparent, and autonomous hiring processes without reliance on centralized intermediaries. The architecture incorporates blockchain-based identity verification, smart contracts for immutable job transactions, and AI-driven candidate matching. The study evaluates the system’s performance through both qualitative and quantitative methodologies, measuring latency, scalability, accuracy, and user trust. While the decentralized nature enhances data security and user autonomy, challenges such as system latency and user onboarding remain. The findings suggest that decentralized AI-driven recruitment systems offer significant advantages in privacy protection and security but require further optimization for mainstream adoption. Future research will explore federated learning, enhanced scalability, and broader applications in freelancing and gig economies.