Audit probability policy subgroup equity

What it's for

Makes trusted AI and management decisions auditable by showing where an aggregate policy is differently calibrated or differently wrong across governed groups—while enforcing support, privacy, uncertainty, and multiple-testing gates.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "bootstrap_draws": 200,
  "maximum_allowed_disparities": {
    "brier_score": 0.05,
    "calibration_gap": 0.05,
    "false_positive_rate": 0.1,
    "positive_predictive_value": 0.1,
    "selection_rate": 0.1,
    "true_positive_rate": 0.1
  },
  "observations": [
    {
      "decision": 0,
      "group_id": "reference",
      "id": "equity-reference-0",
      "outcome": 0,
      "predicted_probability": 0.25
    },
    {
      "decision": 1,
      "group_id": "reference",
      "id": "equity-reference-1",
      "outcome": 1,
      "predicted_probability": 0.75
    },
    {
      "decision": 0,
      "group_id": "reference",
      "id": "equity-reference-2",
      "outcome": 0,
      "predicted_probability": 0.25
    },
    {
      "decision": 1,
      "group_id": "reference",
      "id": "equity-reference-3",
      "outcome": 1,
      "predicted_probability": 0.75
    },
    {
      "decision": 0,
      "group_id": "reference",
      "id": "equity-reference-4",
      "outcome": 0,

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "Groups, reference, metric definitions, decision threshold, disparity tolerances, and privacy minimums are governed before outcomes are inspected.",
    "Rows are independent at the declared aggregate decision unit or have already been clustered to that unit; bootstrap resampling stays within groups.",
    "Observed outcome differences may reflect need, access, history, measurement, or policy and do not by themselves identify discrimination or mechanism."
  ],
  "comparisons": [
    {
      "absolute_disparity": 0,
      "confidence_interval": [
        -0.2,
        0.2
      ],
      "disparity": 0,
      "group_id": "comparison",
      "group_value": 0.5,
      "material_supported_violation": false,
      "maximum_allowed_disparity": 0.1,
      "metric": "selection_rate",
      "p_value": 1,
      "q_value": 1,
      "reference_value": 0.5
    },
    {
      "absolute_disparity": 0,
      "confidence_interval": [
        0,
        0
      ],
      "disparity": 0,
      "group_id": "comparison",
      "group_value": 1,
      "material_supported_violation": false,
      "maximum_allowed_disparity": 0.1,
      "metric": "true_positive_rate",
      "p_value": 1,
      "q_value": 1,
      "reference_value": 1
    },
    {
      "absolute_disparity": 0,
      "confidence_interval": [
        0,
        0

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze the decision unit, eligible population, group vocabulary, reference policy, outcome horizon, six metric definitions, practical disparity tolerances, and privacy minimums before inspecting group results.
2. 2 Calculate weighted selection rate, true-positive rate, false-positive rate, positive predictive value, Brier score, and signed calibration gap for every privacy-eligible group and the governed reference.
3. 3 Resample observations within each group, recompute every disparity against the same explicit or overall reference, derive percentile intervals, and form null-centered bootstrap significance values.
4. 4 Apply Benjamini-Hochberg correction across every group-metric comparison and flag only disparities that exceed the practical tolerance, exclude zero at the governed confidence level, and survive the false-discovery threshold.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "audit an aggregate probabilitydriven policy across" }
  → finds "audit_probability_policy_subgroup_equity"

gitrevio_capability_describe
  { "capability_id": "audit_probability_policy_subgroup_equity" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "audit_probability_policy_subgroup_equity", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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