RAIDP02-BP03 Validate the quality of human and generated labels and features in your dataset

Set up quality control for human annotators by creating training processes that teach consistent labeling, measuring how well different annotators agree with each other, and checking for unwanted biases in their work. Build simple tests using examples with known correct answers to catch annotators who aren't following guidelines or who might be introducing their own biases into the labels.

Hunt for sources of human bias in your labeling process by looking at whether certain annotators consistently label some groups differently than others, whether the annotation guidelines accidentally encourage biased decisions, or whether the examples themselves push annotators toward unfair judgments.

Check synthetic and model-generated labels against human judgment by reviewing a sample of machine-generated labels to see how often they're wrong or misleading. Test whether your synthetic labels work well for underrepresented groups and edge cases where automated systems may fail, and document the specific limitations you discover.

Track how your annotators perform over time by measuring their consistency, accuracy, and agreement with other annotators across different batches of work. Set up alerts that flag when someone's quality drops or when they start showing new bias patterns, so you can provide additional training or feedback before it affects too much data.

Build feedback loops that maintain consistent labeling standards by giving annotators regular updates on their performance, sharing examples of good and bad labels, and updating your guidelines when you discover new edge cases or bias sources. Create processes for fixing labels that don't meet your quality standards to block similar problems in future annotation work.