How do TensorFlow systems behave under feedback loops in recommendation systems?

Feedback loops occur when model predictions influence future training data, reinforcing bias.

In recommendation systems, TensorFlow models influence what users see, and user interactions become training data. This creates a feedback loop where popular items become more popular, while niche items disappear. Over time, this causes distribution shift and model bias amplification.