Feedback Loop
A feedback loop captures outcomes and uses them to refine future decisions.
Definition
Core meaning
A feedback loop captures what actually happened after a decision was executed and uses that result to change how the next similar decision is evaluated. It is the difference between a system that is configured once and a system that improves.
In practice
What the loop needs to capture
Feedback requires linking an outcome back to the specific decision that caused it, which is harder than it sounds and is where most implementations stop.
- The decision identifier, so the outcome can be attributed to it
- The action taken and the rule or model version that chose it
- The measurable result, including the cases where nothing happened
- Whether a human overrode the decision, and what they did instead
Boundaries
Commonly confused with
Feedback is often conflated with observability, which watches the system rather than its judgement.
- Not monitoring: monitoring tells you the system is running, feedback tells you it is right
- Not model retraining alone: retraining is one response to feedback, not the loop
- Not reporting: a report is read by a person, feedback changes evaluation directly
- Not an audit log: the log records what happened, feedback acts on the pattern
Diagnostic
How to tell whether yours closes
The override rate is the most informative signal an operation has about its own decision quality, and it is usually uncaptured.
- Can you measure the outcome of a decision made last month?
- Do you know how often humans override the automated decision?
- When an override happens, is the reason recorded in a form you can aggregate?
- Has any threshold or rule changed as a result of measured outcomes?
Category link
Operational impact
Without a feedback loop the system cannot tune decision quality, and its accuracy silently degrades as conditions drift away from the ones it was configured for. A high override rate is not a failure of the operators — it is the loop telling you the evaluation logic no longer matches reality.
FAQ
Frequently Asked Questions
How quickly does feedback need to arrive?
It should match the decision's own horizon. A fraud decision may be confirmed within minutes; a maintenance decision may take months to prove right or wrong. Design for the slower case: attribute the outcome to the decision durably enough that it survives the gap, rather than assuming feedback arrives while the context is still in memory.
What if outcomes are ambiguous?
Use the human override as your proxy. When an operator changes an automated decision, that is a labelled example of the system being wrong in a way somebody could articulate — which is more valuable than most explicit outcome measures, and far cheaper to collect. Capture the override reason as structured data, not free text.
Does a feedback loop mean the model retrains itself?
Not necessarily, and automatic retraining is often the wrong default. Feedback frequently surfaces that a threshold is miscalibrated or that a rule has an unhandled exception — both fixed by editing logic, not by retraining. Automatic retraining without review can also entrench a bias the loop was meant to reveal.
Operational Context
See how this concept appears in real operational systems
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