Feedback Loops in ML Systems

A model often influences the environment in which its future data are collected. A recommendation system chooses what users see, a fraud system directs which cases receive review, and an allocation model determines who gets an opportunity. These decisions affect observed outcomes. When those outcomes are fed back into training, the resulting dataset reflects both the underlying world and the model's prior actions. In a recommender, highly ranked items receive more exposure and therefore more opportunities for clicks. A click log can make those items appear more relevant, reinforcing their ranking. In hiring or lending, only selected applicants may receive a downstream outcome such as an interview or repayment observation, leaving outcomes for unselected cases missing. These are selection effects; treating missing outcomes as negative or equivalent can bias future models. Feedback loops do not always amplify bias, and their direction depends on policy, users and data collection. A model may change behavior beneficially, or repeated exposure may reduce diversity or hide alternatives. Separate exposure from preference where possible by logging what was shown, position and selection probability. Use randomized exploration, holdouts or carefully designed experiments to learn about alternatives when ethical and safe. Off-policy evaluation can estimate some changes from logged data under assumptions, but it is sensitive to missing support and inaccurate propensities. Monitor who receives opportunities, what is observed and which cases lack outcomes. Compare label coverage across scores and groups. Consider delayed effects and user adaptation. Changes to exploration can affect short-term metrics, so define guardrails and review risks. Repeatedly training on model-influenced data can entangle policy and prediction; maintain clear logs and versioned decision rules. A feedback loop is a property of the whole sociotechnical system, not solely of a model algorithm. Mitigation requires measurement of actions and outcomes, not merely retraining on a larger dataset.