Concern 90 · Working draft · editorial review in progress
Feedback loop drift
Feedback-loop drift happens when a model's decisions change the data it will later learn from or be evaluated on.
Understanding the concern
A recommendation changes what people see; their responses then become new evidence for the recommender. The system is no longer simply observing a fixed world. It is helping create the pattern it observes.
This can make a model appear to confirm its own assumptions. Research on performative prediction studies such effects and the conditions under which retraining behaves well. More recent data is not automatically independent evidence.
An example
A shop recommends one product more often, then interprets its higher sales as proof that it was always customers' favorite.
Illustrative unless identified as a documented case.
Words in plain English
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Drift
- A change over time in data or real-world conditions that can make a system less reliable.
- Feedback loop
- A process in which an outcome becomes an input to later decisions. Mistakes can grow when the same pattern is repeatedly reinforced.
Evidence and its limits
The paper models predictions that influence subsequent outcomes and the limits of repeated retraining.
Perdomo and colleagues · ICML 2020 · Research study
Performative Prediction ↗ (opens in a new tab)Studies settings where deploying predictions changes the population being predicted; stability depends on assumptions.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
How much of the new pattern was caused by our own earlier recommendations?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Humility
Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.
State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.
Ask together: What might we be missing, and who could help us see it?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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