Concern 296 · Working draft · editorial review in progress
User-feedback loops
User-feedback loops can distort a model when the feedback used to improve it is incomplete, biased, or shaped by the model's own behavior.
Understanding the concern
People who leave ratings may differ from those who stay silent. A quick thumbs-up can reflect friendliness or convenience rather than accuracy. If the system learns mainly from those signals, it may reinforce popular answers or overlook people whose needs were not met.
Feedback remains valuable when its meaning and limits are understood. Different signals can answer different questions, and some outcomes require direct checking. Teams should examine who is represented, what behavior is rewarded, and whether later changes improve the intended result.
An example
A service learns mainly from enthusiastic users while people who could not use it successfully leave without rating it.
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.
Evidence and its limits
The sources support preference and selection mechanisms; the example illustrates a possible feedback bias rather than a universal outcome.
Sharma and colleagues · 2023 · Research study
Towards Understanding Sycophancy in Language Models ↗ (opens in a new tab)Investigates models favoring users' stated beliefs over truthful responses and the role of preference judgments.
Suresh and Guttag · EAAMO 2021 · Research framework
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle ↗ (opens in a new tab)Framework distinguishes historical, representation, measurement, aggregation, learning, evaluation, and deployment bias. These categories can overlap.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
A question to discuss
Whose experience is missing from the feedback, and what does each rating actually measure?
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.
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
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?
Cooperation
Cooperation means working with others toward a shared purpose while respecting their contribution. It is not simply asking people to accept a decision already made.
Share information, roles, and credit. Include the people who do the daily work when choosing or testing a tool. Make disagreements discussable so that cooperation does not become pressure to agree.
Ask together: What can we do together that none of us can do well alone?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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