Fairness metrics calculator

What does the fairness metrics calculator produce?

Calculate selection, true-positive and false-negative rates for two your groups without deciding whether an observed difference is lawful or fair.

The working area above saves numeric inputs in this browser and exposes every formula. It does not decide fairness, safety, compliance or release approval.

What do you need before you start?

• Freeze the complete population, process, period and approved outcome criteria before entering aggregate counts.
• Use ordinary nonnegative decimal notation. Counts must be whole numbers through one billion. Review thresholds support increments of 0.1 percentage points.
• Confirm comparable populations and source labels. The tool does not collect individual records or test an AI model.

What information do you enter?

The browser calculator accepts only the aggregate numeric inputs listed below.

• Reference group records (required). Enter the complete assessed population for the reference group.
• Reference group favorable outcomes (required). Enter favorable outcomes for the reference group.
• Reference group actual positives (required). Enter records independently classified as actual positives.
• Reference group true positives (required). Enter actual positives that received a favorable outcome.
• Comparison group records (required). Enter the complete assessed population for the comparison group.
• Comparison group favorable outcomes (required). Enter favorable outcomes for the comparison group.
• Comparison group actual positives (required). Enter records independently classified as actual positives.
• Comparison group true positives (required). Enter actual positives that received a favorable outcome.

What you can download

Every result must identify its supplied facts, assumptions and unresolved items.

How does the fairness metrics calculator work?

1. 1
   Freeze the evidence basis

   Retain the source population, evaluation version, success criterion and review thresholds in your own controlled records.

2. 2
   Enter the bounded counts

   Use the numeric fields above or a clearly fictional example. Correct counts that exceed their population or imply an impossible confusion matrix.

3. 3
   Calculate and inspect

   Review every descriptive rate and entered gate. Gates use exact count and decimal expressions. Displayed percentages round to two decimal places.

4. 4
   Keep a restorable backup

   Download the free backup before clearing or replacing work. Earlier JSON result downloads can restore inputs. Restoring recalculates results and preserves previous browser inputs.

5. 5
   Prepare the finalized calculation

   Download all seven finalized files free. Browser preparation does not transfer inputs to a provider.

Method and sources

Apply the steps above to the supported inputs. Use the listed source sections to review the rule basis, assumptions and limits.

• Common fairness metricsSelection and error-rate definitions, and the need to interpret metrics in context. Demographic parity, equalized odds and limits of the four-fifths screen.Fairlearn project. Reviewed 2026-10-07.
• Binary confusion matrix definitionsTrue positive, false positive, false negative and true negative count definitions. Binary classification confusion matrix.scikit-learn project. Reviewed 2026-10-07.

Common questions

What still needs review?

Group definition, data quality, sample design, statistical significance and legal relevance require qualified human review.

Aggregate counts do not establish complete evidence, comparable samples or valid labels. No individual-record audit, model test or release approval is performed.

Rates and entered gates describe the supplied counts. They do not establish statistical significance or a legal conclusion.

Related working records

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