Override a Low-Confidence Model Decision
Design a review queue that helps an operator understand uncertain classifications, make a human decision, and record downstream effects.
The brief
Understand the problem
Background
A confidence value is only useful when reviewers know what decision it refers to, how the threshold was set, and what evidence was missing. An override also needs more than a button because it may affect routing, customer treatment, and later evaluation.
User context
Luis reviews expense reports routed by a classification model. One report is labeled personal expense at 54 percent confidence even though the receipt shows a client dinner and the calendar contains a matching meeting.
Product problem
Operators need to examine case evidence and uncertainty without anchoring on a score, then make a reversible decision with a clear rationale and accountable effect.
Objective
Create queue prioritization, evidence review, override, and outcome monitoring for one low-confidence expense classification.
What to design
Define the experience
Required experience
- Find cases that require review based on uncertainty and operational impact
- Inspect inputs, missing signals, alternative labels, and policy context
- Confirm or override the proposed label with a reason
- Review downstream actions and flag the case for quality analysis
Screens and states
- Review queue
- Case evidence
- Decision and override
- Outcome audit
Core user flow
Follow the critical path
- 01
Luis opens the queue sorted by payment deadline and review need
- 02
He sees the proposed label only after reviewing receipt, merchant, attendee, and policy context
- 03
He selects client meal and records the matching calendar event
- 04
The interface previews that the change will resume reimbursement and remove a manager exception
- 05
Luis confirms the override and sends the case to a sampled quality set
Product rules
Requirements and constraints
Requirements
- Pair confidence with calibration context, decision threshold, alternatives, and missing inputs
- Avoid visual treatment that makes the proposed label appear authoritative by default
- Show policy evidence and case evidence separately
- Require rationale proportionate to the impact of an override
- Record downstream state changes, reversibility, reviewer, and later quality outcome
Constraints
- Reviewers must not infer protected characteristics from unrelated profile data
- Confidence values from different model versions are not directly comparable
- An override cannot silently update policy or retrain a model
Reality check
States worth considering
Finish line
What to deliver
- Four desktop screens showing evidence-first review, a human override, and downstream audit
Optional direction
Visual resources
Use these as a starting constraint if you want one. They are not part of the required solution.
Amatic SC & Josefin Sans
Clear interface writing gives people the confidence to understand what changed and decide what to do next.
Amatic SC & Josefin Sans
Clear interface writing gives people the confidence to understand what changed and decide what to do next.