How do you handle feedback loops and bias in a recommendation system?

A recommender trains on data its own past recommendations produced, so it learns to confirm its own beliefs. The signal is recognizing the loop, naming the biases it breeds, and knowing the exploration and debiasing fixes that break it.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.