Remote data annotation vetting

https://spark-wiki.win/index.php/Remote_data_annotation_vetting

Remote data annotation vetting involves reviewing and verifying the accuracy of labels assigned to data by remote annotators. This process ensures the quality and reliability of training data used for machine learning models, with the goal of flagging errors in labeling and consistency, making it crucial for organizations that depend on precise data for AI development.