Practical writing on annotation, data quality and building reliable AI datasets, from the people who do the work.
Trustworthy AI data comes from five practices: clear guidelines, real domain expertise, measured agreement between annotators, documented quality checks, and honest records of where the data came from. This article explains each one and lists questions to ask any data partner.
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