# Data Quality Control Big AI companies need to do reinforcement learning from human feedbacks, to get high-quality labeling on images and other media data to train AIs for accuracy. Some companies usually do this in regions where labor is cheap. Labeling tasks like medical or legal, need expert to complete the job. It is crucial to verify that the labelers have sufficient knowledge and skills to accurately label in these domains, e.g., by proving they are indeed an MD or JD. zkTLS-based data proofs are an efficient way to verify the eligibility of labelers and weed out the bad actors lying about their credentials and providing inaccurate training data in these specific domains. Automating credential verification with data proofs can improve the integrity of AI training data sets but also streamline operations, cutting costs and increasing trust in the data pipeline. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://nexaid.gitbook.io/nexaid/implementation-scenarios/data-quality-control.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.