Platform Architecture

The architecture of DataCrowd is designed to facilitate seamless interaction between contributors and requesters through a smooth workflow.

Core Components:

• Task Management System: Advanced algorithms automatically assign tasks to contributors based on their skills and past performance. This ensures optimal task allocation, improving efficiency and productivity.

• Validation Mechanism: Each annotation task undergoes a rigorous validation process to maintain quality. This process includes algorithm-based automatic checks and community-driven validation, allowing collective oversight.

• Reward Distribution Engine: The platform ensures instant and transparent reward distribution via smart contracts, guaranteeing contributors are paid fairly and promptly.

User Roles:

• Contributors: Individuals or organizations who are capable of data labeling. They receive tasks according to their expertise and are rewarded for their contributions.

• Requesters: Individuals or organizations that require labeled data for AI training. These are usually companies like established tech companies and emerging startups.

Workflow Overview:

• Task Creation: Requester submit tasks with specific requirements (e.g., data type, labeling type).

• Task Assignment: The system assigns tasks to suitable contributors based on a matching algorithm.

• Data Labeling: Contributors complete tasks and provide labeled data according to specified standards.

• Validation: The completed labels undergo quality assurance review.

• Reward Distribution: Contributors receive rewards through an automated process, fostering a fair economic ecosystem.