How to Understand and Improve the Quality of Annotated Data

High-quality annotated data is crucial for the performance of machine learning models. To improve data quality, it is essential to first understand and measure it, often through metrics that track consistency and accuracy. Key strategies for enhancement include developing clear annotation guidelines, implementing robust quality assurance (QA) processes, and utilizing feedback loops to continuously train human annotators. By systematically addressing these areas, organizations can overcome data bottlenecks and build more reliable AI systems.