What are some real-world examples of data drift in AI models?

What are some real-world examples of data drift in AI models?

Data drift occurs when the statistical properties of the target variable change over time. For example, a model predicting house prices may become less accurate if the housing market shifts due to economic changes.

Can you give me a specific case where this happened?

Sure! One notable case is the credit scoring models used by banks. When the economic conditions changed during the 2008 financial crisis, many models failed to predict defaults accurately because they were trained on data from a more stable period.

How can companies detect data drift?

Companies can use statistical tests to compare the distributions of incoming data with the training data. Monitoring key performance indicators (KPIs) can also help identify when a model's performance starts to degrade.

What steps can be taken to mitigate data drift?

To mitigate data drift, companies can retrain models regularly with new data, implement continuous monitoring systems, and use adaptive learning techniques that allow models to adjust to new patterns.