Effective Quality Assurance for Data Labels through Crowdsourcing and Domain Expert Collaboration

Researchers and scientists have been using crowdsourcing platforms to collect labeled training data in recent years. The process is cost-effective and scalable, but research has shown that the quality of truth inference is unstable due to worker bias, work variance, and task difficulty. In this demonstration, we present a hybrid system, named IDLE (Integrated Data Labeling Engine), that brings together a well-trained troop of domain experts and themultitudes of a crowdsourcing platform to collect high-quality training data for industry-level classification engines. We show how to acquire high quality labeled data through quality control strategies that dynamically and cost-effectively leverage the strengths of both domain experts and crowdsourcing.