in the company where I work we retrain ML models regularly every day. Now we started to experiment with retraining a model by new observations with labels predicted by model itself.
I've tried to search on the Internet if someone else uses this technique but I didn't find much.
My gut feeling tells me that it is not the right approach as the labels do not represent ground truth any more and therefore the model might deviate and learn an incorrect hypothesis in time.
Am I right that this approach is not correct? Could you please direct me to some sources where I can read more about this topic?