Public-level explanation only.
Candidate-loss risk in AI workflows.
The risk is not average AI performance alone. The risk is losing valuable, rare, or weak-signal candidates before review can see that they mattered.
Problem
Valuable, rare, weak-signal, or emerging cases can be discarded too early. Once they are discarded, later review may only see the surviving path.
Governance question
Can the workflow prevent uncertain states from becoming premature approval, execution, rejection, or responsibility closure?
Failure modes to look for
- Premature reject: a candidate is discarded before enough evidence exists.
- Premature commit: a system moves forward as if confidence were justified.
- Invisible residue: weak signals remain unresolved and reappear as repeated review.
- Responsibility closure: accountability closes before the boundary condition has been examined.
Bounded reproduction test
- Define the workflow boundary and decision points.
- Identify where candidates are adopted, rejected, returned, escalated, or closed.
- Compare baseline review against a governed HOLD / re-evaluation workflow.
- Measure missed handling, wrong return, review load, over-HOLD, and unresolved residue.
Public boundary
This public overview does not request confidential workflows, private architecture, customer data, production logs, source code, model weights, or implementation details.
Exact thresholds, guard conditions, seed-level implementation, precise metric ratios, and patent-sensitive mappings remain outside this public page.