Feedback Loop
A process where AI outputs are fed back as inputs, which can amplify model behavior - for better (reinforcement learning) or worse (bias reinforcement).
Describes both intentional (reinforcement learning) and unintentional (recommendation reinforcement) loops. Positive loops can optimize performance over time, but negative loops risk bias amplification - e.g., a recommender showing popular content makes it more popular. Governance strategies include loop-detection metrics, intervention policies (diversity quotas), and simulated-loop testing before live deployment.
A news platform’s recommender shows trending articles. Because users click more on those, the system further amplifies them, narrowing content diversity. The team introduces “serendipity” constraints - injecting less-clicked topics at a fixed rate - to break the unbounded feedback loop and maintain content variety.
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