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Guardrail False Block Rate Calculator

Work out guardrail false block rate instantly with clear inputs, formula shown and shareable results.

About Guardrail False Block Rate Calculator

A guardrail has two error rates that must be tracked separately. The false block rate is wrongly blocked requests over all requests, which is what users experience as the product refusing reasonable work. Guardrail precision is the share of blocks that were justified. A guardrail blocking 2.4 percent of traffic with 15 percent of those wrong is denying service to 360 legitimate users per 100,000.

Formula

Guardrail error rates

false block rate = wrongly blocked / total requests; precision = (blocked - wrongly blocked) / blocked

Worked example

With the default values below, Guardrail False Block Rate Calculator returns False block rate of 0.360%. Change any input above and every figure here is recalculated in your browser.

Inputs used

Requests screenedminimum 1
100,000
Requests blockedminimum 0
2,400
Blocked requests later judged safeminimum 0
360

Results

False block rate
0.360%
Overall block rate
2.40%
Guardrail precision
85.00%
False blocks per million requests
3,600

Step by step

  1. 1Values used
  2. 2Guardrail error rates
  3. 3False block rate= 0.360
  4. 4Overall block rate= 2.40
  5. 5Guardrail precision= 85.00
  6. 6False blocks per million requests= 3,600

Assumptions and limits

  • The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 3 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Which error should I optimise for?

It depends on the harm. For serious safety categories, accept a higher false block rate; for style or tone policies, precision matters more because over-blocking damages the product.

How do I measure blocked-but-safe reliably?

Human review of a random sample of blocks. Reviewing only appeals biases the estimate, because most wrongly blocked users never appeal.

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