Outcome labels
Outcome labels associate examples with a recorded or assessed result. A label might indicate that a task was completed correctly, a fault recurred, or a request required escalation. The meaning depends on the rule and observation period used to assign it; an application status is not necessarily the final business outcome.
How it works
Define the outcome, the evidence required, and when it becomes observable. Connect the label to the correct case and document uncertain, missing, or later-corrected results. A completed invoice, for example, may not establish whether a repair remained effective.
When using outcomes as prediction targets, keep later information out of the inputs available at prediction time. Otherwise, the dataset can make a task appear easier than it would be in practice.
Why it matters for licensing
Outcome information can help recipients understand whether an action led to the intended result. Its usefulness depends on consistency, coverage, and timing. Licensing documentation should distinguish measured outcomes from inferred or manually judged labels.
Example
Fictional example: A maintenance dataset labels whether a fault recurred within a defined follow-up period. Records without enough follow-up are marked unknown rather than automatically counted as successful repairs.
Limitations and misconceptions
A measured outcome does not establish which action caused it. Missing follow-up, selective reporting, and changing definitions can distort labels. Outcomes also may reveal sensitive facts and need appropriate disclosure review.
Questions to ask
- What evidence and observation period determine the outcome?
- How are unknown, corrected, or ambiguous results represented?
- Could later outcome information leak into the model’s input?
Sources
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