Control and Monitoring of Artificial Intelligence Algorithms
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This paper elucidates the importance of governing an artificial intelligence model post-deployment and overseeing potential fluctuations in the distribution of present data in contrast to the training data. The concepts of data drift and concept drift are explicated, along with their respective foundational distributions. Furthermore, a range of metrics is introduced, which can be utilized to scrutinize the model's performance concerning potential temporal variations.
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