What is meant by data drift in AI systems?

Data drift refers to the phenomenon where the statistical properties of the input data to an AI system change over time, which can lead to declining performance of the model if it is not updated or retrained. When the underlying patterns in the data shift, the AI model may struggle to make accurate predictions or classifications because it was trained on a different dataset that no longer reflects the current data landscape.

This can occur due to various factors, such as changes in consumer behavior, external environmental influences, or technological advancements that alter how data is collected. Recognizing and addressing data drift is crucial in maintaining the accuracy and reliability of AI systems, ensuring they can adapt to new data patterns and continue providing valuable insights.

The other choices do not accurately characterize data drift. Static performance of AI systems does not account for any potential changes in data over time. Continuous improvement of machine learning algorithms is related but does not directly define data drift; rather, it is about enhancing the model itself, not the data it processes. Increased regulatory oversight refers to external factors affecting AI governance and compliance, not directly tied to data pattern changes.