【国外标准】 Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 4: Data quality process framework

Establishes general common organizational approaches, regardless of the type, size, or nature of the applying organization, to ensure data quality for training and evaluation in analytics and machine learning (ML). It includes guidance on the data quality process for: supervised ML with regard to the labeling of data used for training ML systems, including common organizational approaches for training data labeling; unsupervised ML; semi-supervised ML; reinforcement learning; analytics.