Synonyms: Distributional Shift.
Once trained, most machine learning systems operate on static models of the world that have been built from historical data which have become fixed in the systems’ parameters. This freezing of the model before it is released ‘into the wild’ makes its accuracy and reliability especially vulnerable to changes in the underlying distribution of data. When the historical data that have crystallised into the trained model’s architecture cease to reflect the population concerned, the model’s mapping function will no longer be able to accurately and reliably transform its inputs into its target output values. These systems can quickly become prone to error in unexpected and harmful ways. In all cases, the system and the operators must remain vigilant to the potentially rapid concept drifts that may occur in the complex, dynamic, and evolving environments in which your AI project will intervene. Remaining aware of these transformations in the data is crucial for safe AI.
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