Threshold testing

How it works

Many AI behaviours hinge on a threshold: refuse versus answer, flag versus allow, escalate versus handle. Threshold testing sweeps inputs from clearly-one-side to clearly-the-other and locates the transition, then asks whether it sits where policy intends. It surfaces both over-refusal (the boundary set too tight, blocking benign requests) and under-refusal (set too loose), and the unstable middle band where small changes flip the verdict.

When to use it

Tuning and auditing safety filters, content classifiers, and any allow/deny or confidence cutoff; diagnosing over- and under-refusal; run again whenever a filter's threshold or training data changes, since the boundary this method maps can shift without any change to the model sitting behind it.

Limitations

Boundaries shift between versions and have to be re-mapped after upgrades, and a single threshold can hide very different behaviour across different request types or topics — a refusal boundary mapped on one category doesn't transfer to another. The unstable middle band itself can also be inherently noisy, making the exact transition point harder to pin down than the sweep suggests.

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