Audit attrition risk prediction integrity

Audit an attrition model's complete eligible cohort, point-in-time features, supportive-use governance, intervention-contaminated labels, competing outcomes, calibration, false positives and authorized subgroup error before any person-level use.

Forecasting & survival Audit CTOs Tech CEOs IT managers Team leads Investors

What it's for

The audit to run before an attrition model touches a person. It checks cohort completeness, point-in-time features, label contamination, calibration and subgroup error first.

Turns Gitrevio's person-level attrition promise into an auditable capability: complete cohorts, real later outcomes, no future leakage, calibrated probabilities and an explicit ban on adverse automated action.

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.

Field	Type	Role	Required
as_of_ms	number ≥ 0	Your calibration	Yes
eligible_cases	array of objects (7 fields)	Evidence	Yes
evidence_artifacts	array of objects (4 fields)	Evidence	Yes
feature_snapshots	array of objects (10 fields) ≥ 0 items	Evidence	Yes
max_detail_rows	integer ≥ 1, ≤ 500	Numerical control	Optional
maximum_calibration_error	number ≥ 0, ≤ 1	Your calibration	Optional
maximum_false_positive_rate	number ≥ 0, ≤ 1	Your calibration	Optional
maximum_multiclass_brier	number ≥ 0, ≤ 2	Your calibration	Optional
maximum_subgroup_calibration_gap	number ≥ 0, ≤ 1	Your calibration	Optional
minimum_mature_outcomes	integer ≥ 1	Your calibration	Optional
model_versions	array of objects (16 fields)	Evidence	Yes
predictions	array of objects (11 fields) ≥ 0 items	Evidence	Yes
resolved_outcomes	array of objects (8 fields) ≥ 0 items	Evidence	Yes
voluntary_alert_threshold	number ≥ 0, ≤ 1	Your calibration	Optional

Each `model_versions` record

Field	Type	Required
access_restricted	boolean	Yes
correction_process_available	boolean	Yes
effective_from_ms	number (≥ 0)	Yes
effective_until_ms	number,null (≥ 0)	Yes
employee_notice_provided	boolean	Yes
evidence_verified	boolean	Yes
feature_ids	array of string	Yes
frozen_at_ms	number (≥ 0)	Yes
horizon_periods	integer (≥ 1, ≤ 104)	Yes
id	string (non-empty)	Yes
independently_reviewed	boolean	Yes
intended_use_id	any	Yes
lawful_purpose_documented	boolean	Yes
prohibited_attribute_ids	array of string	Yes
scope_id	string (non-empty)	Yes
trained_through_ms	number (≥ 0)	Yes

Example input

{
  "as_of_ms": 500,
  "eligible_cases": [
    {
      "audit_group_id": "group-b",
      "audit_group_use_authorized": true,
      "eligible": true,
      "evidence_verified": true,
      "id": "opaque-0",
      "prediction_at_ms": 100,
      "scope_id": "company"
    },
    {
      "audit_group_id": "group-a",
      "audit_group_use_authorized": true,
      "eligible": true,
      "evidence_verified": true,
      "id": "opaque-1",
      "prediction_at_ms": 101,
      "scope_id": "company"
    },
    {
      "audit_group_id": "group-b",
      "audit_group_use_authorized": true,
      "eligible": true,
      "evidence_verified": true,
      "id": "opaque-2",
      "prediction_at_ms": 102,
      "scope_id": "company"
    },
    {
      "audit_group_id": "group-a",
      "audit_group_use_authorized": true,
      "eligible": true,
      "evidence_verified": true,
      "id": "opaque-3",
      "prediction_at_ms": 103,
      "scope_id": "company"
    },
    {
      "audit_group_id": "group-b",
      "audit_group_use_authorized": true,
      "eligible": true,
      "evidence_verified": true,

Truncated for display — the full payload is 2286 lines.

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.

Example output

{
  "configuration": {
    "as_of_ms": 500,
    "maximum_calibration_error": 0.1,
    "maximum_false_positive_rate": 0.25,
    "maximum_multiclass_brier": 0.35,
    "maximum_subgroup_calibration_gap": 0.1,
    "minimum_mature_outcomes": 30,
    "voluntary_alert_threshold": 0.5
  },
  "decision": "supportive_use_ready",
  "failed_gates": [],
  "guardrails": [
    "This audit reconstructs performance only from mature, verified and intervention-uncontaminated outcomes; an alert that triggered a successful intervention is not a natural-history negative label.",
    "Protected-group labels may be used only for authorized error auditing. They are never model features, rankings or employment criteria.",
    "Supportive-use readiness does not authorize hiring, firing, compensation, promotion, surveillance, security or disciplinary action. People receive notice, correction access and meaningful human review."
  ],
  "method": "point_in_time_attrition_prediction_integrity_audit_v1",
  "subgroup_diagnostics": [
    {
      "audit_group_id": "group-a",
      "mature_case_count": 20,
      "voluntary_exit_ece": 0.02
    },
    {
      "audit_group_id": "group-b",
      "mature_case_count": 20,
      "voluntary_exit_ece": 0.06
    }
  ],
  "summary": {
    "eligible_case_count": 40,
    "intervention_contaminated_case_count": 0,
    "mature_uncontaminated_case_count": 40,
    "model_version_count": 1,
    "prediction_count": 40,
    "unmatured_or_missing_outcome_count": 0
  },
  "validation": {
    "maximum_subgroup_calibration_gap": 0.04,
    "multiclass_brier": 0.0072,
    "multiclass_log_loss": 0.0727,
    "voluntary_alert_false_positive_rate": 0,
    "voluntary_exit_ece": 0.04

Truncated for display — the full payload is 46 lines.

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1 Freeze the model version effective at every prediction and reconcile exactly one prediction plus one explicit snapshot of every declared feature for every eligible case.
2 Reject future observations, unverified artifacts, prohibited or proxy attributes, adverse automation, missing notice/correction/access controls and labels changed by a retention intervention.
3 Reconstruct multiclass Brier, voluntary-exit calibration, alert false positives and authorized subgroup gaps from mature untreated outcomes rather than trusting self-reported validation summaries.

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.

Assumptions & guardrails

Training examples precede their outcomes, censoring and unresolved work are represented, and deployment populations remain comparable to validation cohorts.
Eligibility includes scored and unscored cases, event labels distinguish voluntary exit, internal transfer and involuntary exit, intervention timing is complete, and audit groups are lawful fairness-only fields.
A predictive interval or risk estimate is not a deadline promise, causal explanation, or individual-performance judgment.
A passing audit authorizes only supportive human-reviewed use; it never authorizes hiring, firing, compensation, promotion, surveillance, security or disciplinary action.

Minimum evidence

model_versions: required and organization-defined
eligible_cases: required and organization-defined
predictions: at least 0 rows/items
feature_snapshots: at least 0 rows/items
resolved_outcomes: at least 0 rows/items
evidence_artifacts: required and organization-defined
as_of_ms: required and organization-defined

How to validate it

Use chronological train/calibration/test windows, compare proper scores and decision value with a simple baseline, and recalibrate only from outcomes resolved after prediction time.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

one point-in-time prediction spine joining every eligible case to exactly one effective model, every declared present-or-explicitly-missing feature, its immutable artifact, and a later verified untreated outcome
supportive purpose, lawful basis, employee notice/correction/access, feature and prohibited-proxy registry, eligibility and event definitions, outcome maturity, intervention contamination, alert threshold, Brier/calibration/false-positive gates, audit-group authorization and human review

Calibration workflow

1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
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 attrition models complete eligible" }
  → finds "audit_attrition_risk_prediction_integrity"

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

gitrevio_capability_run
  { "capability_id": "audit_attrition_risk_prediction_integrity", "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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