Quality assurance is critical in data annotation service to ensure the accuracy and reliability of labeled data. Implementing multiple layers of quality control, such as cross-validation, consensus scoring, and random sampling, can help identify and correct errors. Using experienced annotators, providing clear guidelines, and offering ongoing training are also essential practices. High-quality annotated data leads to better-performing machine learning models and more reliable outcomes.