How to deal with a machine learning model which affects future ground truth data?
One option is reinforcement learning (RL).
RL frames the problem as sequential decision-making under uncertainty. There is an agent making decisions and after the decision is made, the agent collects new data. The next round of decisions uses the new data.
What you are describing is a scenario where the distribution of &quot;ground-truth&quot; changes over time. Many statistical and machine learning models assume a stationary process where the underlying distribution does not change over time. Some types of RL does not assume a stationary process.