Forecast AI data rights liability

What it's for

Turns vague AI data anxiety into a board-ready risk distribution: likely loss, correlated tail, affected value, control benefit and the exact exposures driving the result.

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.

{
  "random_seed": 47,
  "rights_exposures": [
    {
      "affected_units": 1000,
      "annual_value_supported": 250000,
      "asset_group_id": "support-corpus",
      "common_correlation": 0.4,
      "containment_fraction": 0.5,
      "defect_alpha": 2,
      "defect_beta": 18,
      "detection_probability": 0.7,
      "evidence_verified": true,
      "fixed_response_cost": 10000,
      "id": "support-retrieval-eu",
      "jurisdiction_id": "eu",
      "severity_log_mean": 4,
      "severity_log_sigma": 0.5,
      "value_disruption_fraction": 0.2
    }
  ],
  "scenarios": [
    {
      "defect_odds_multiplier": 1,
      "disruption_multiplier": 1,
      "id": "base",
      "probability": 0.8,
      "severity_multiplier": 1
    },
    {
      "defect_odds_multiplier": 2,
      "disruption_multiplier": 1.5,
      "id": "adverse-rights-regime",
      "probability": 0.2,
      "severity_multiplier": 2
    }
  ],
  "simulation_count": 2000
}

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": [
    "Defect priors are calibrated from comparable adjudicated rights reviews, not guessed from repository activity or model output.",
    "A Gaussian copula represents shared asset-group defects and severity while scenarios coherently move odds, harm and disruption for every exposure.",
    "Lognormal severity, detection benefit and containment effectiveness are decision-model assumptions that require backtesting and local recalibration."
  ],
  "counts": {
    "asset_groups": 1,
    "exposures": 1,
    "scenarios": 2,
    "simulations": 2000
  },
  "decision": "review_ai_data_rights_tail_exposure",
  "evidence": {
    "unverified_exposure_count": 0,
    "verified_exposure_fraction": 1
  },
  "exposure_forecasts": [
    {
      "asset_group_id": "support-corpus",
      "evidence_verified": true,
      "expected_loss": 11143.8413,
      "exposure_id": "support-retrieval-eu",
      "jurisdiction_id": "eu",
      "posterior_mean_defect_probability": 0.1,
      "probability_of_loss": 0.117
    }
  ],
  "forecast": {
    "conditional_value_at_risk": 118892.6682,
    "expected_loss": 11143.8413,
    "maximum_simulated_loss": 219692.0129,
    "median_loss": 0,
    "probability_of_any_loss": 0.117,
    "tail_probability": 0.05,
    "value_at_risk": 95780.4812
  },
  "limitations": [
    "This is an internal financial risk forecast, not legal advice, a damages estimate for litigation, or evidence that a rights violation occurred.",
    "Rare regime changes, injunctions, model disgorgement, correlated claimants and unregistered datasets can make the represented tail materially too small."
  ],
  "method": "hierarchical_gaussian_copula_beta_lognormal_ai_data_rights_loss_v1",
  "reproducibility": {
    "random_seed": 47,

How it works

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

1. 1 Convert adjudicated comparable rights reviews into exposure-local Beta defect priors and encode lognormal per-unit severity, fixed response cost and supported-value disruption.
2. 2 Draw one coherent operating scenario and shared asset-group Gaussian factors per simulation, then combine them with exposure-specific risk, detection and containment.
3. 3 Aggregate without assuming exposure independence and report expected loss, probability of loss, VaR/CVaR, evidence quality and exposure-level contributors.

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": "forecast correlated ai datarights loss disruption" }
  → finds "forecast_ai_data_rights_liability"

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

gitrevio_capability_run
  { "capability_id": "forecast_ai_data_rights_liability", "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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