Forecast AI regulatory change liability
Forecast counsel-defined regulatory-change frequency, lognormal remediation work and cost, capacity queues, shared jurisdiction shocks, enforcement exposure and liability CVaR.
What it's for
Gives CEOs, boards and investors an AI-regulation runway forecast: likely change load, remediation backlog, financial exposure and the capacity needed before deadlines.
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 |
|---|---|---|---|
| change_history | array of objects (11 fields) | Evidence | Yes |
| current_obligation_exposures | array of objects (19 fields) | Evidence | Yes |
| horizon_years | number > 0 | Your calibration | Optional |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| minimum_change_events | integer ≥ 1, ≤ 1000000 | Your calibration | Optional |
| minimum_history_rows | integer ≥ 1, ≤ 1000000 | Your calibration | Optional |
| random_seed | integer ≥ 0, ≤ 4294967295 | Your calibration | Optional |
| scenarios | array of objects (10 fields) | Evidence | Yes |
| simulation_count | integer ≥ 1000, ≤ 1000000 | Your calibration | Optional |
| tail_probability | number > 0, < 1 | Your calibration | Optional |
Each current_obligation_exposures
record
| Field | Type | Required |
|---|---|---|
| change_rate_prior_regime_years | number (> 0) | Yes |
| change_rate_prior_shape | number (> 0) | Yes |
| cost_prior_log_mean | number | Yes |
| cost_prior_log_sd | number (> 0, ≤ 5) | Yes |
| cost_prior_strength | number (> 0) | Yes |
| current_system_count | integer (≥ 1, ≤ 1000000) | Yes |
| effort_prior_log_mean_days | number | Yes |
| effort_prior_log_sd | number (> 0, ≤ 5) | Yes |
| effort_prior_strength | number (> 0) | Yes |
| evidence_verified | boolean | Yes |
| fixed_enforcement_loss | number (≥ 0) | Yes |
| id | string (non-empty) | Yes |
| jurisdiction_group_id | string (non-empty) | Yes |
| notice_days_log_mean | number | Yes |
| notice_days_log_sd | number (> 0, ≤ 5) | Yes |
| obligation_class | string (non-empty) | Yes |
| remediation_capacity_effort_days_per_calendar_day | number (> 0) | Yes |
| reusable_control_fraction | number (≥ 0, ≤ 1) | Yes |
| value_disruption_per_noncompliant_system_day | number (≥ 0) | Yes |
{
"change_history": [
{
"change_event_count": 1,
"evidence_verified": true,
"exposure_regime_years": 1,
"id": "regime-00",
"obligation_class": "counsel-high",
"remediation_cost_log_squared_sum": 84.83036976765439,
"remediation_cost_log_sum": 9.210340371976184,
"remediation_cost_observation_count": 1,
"remediation_effort_log_squared_sum": 8.974411854812963,
"remediation_effort_log_sum": 2.995732273553991,
"remediation_effort_observation_count": 1
},
{
"change_event_count": 1,
"evidence_verified": true,
"exposure_regime_years": 1,
"id": "regime-01",
"obligation_class": "counsel-high",
"remediation_cost_log_squared_sum": 84.83036976765439,
"remediation_cost_log_sum": 9.210340371976184,
"remediation_cost_observation_count": 1,
"remediation_effort_log_squared_sum": 8.974411854812963,
"remediation_effort_log_sum": 2.995732273553991,
"remediation_effort_observation_count": 1
},
{
"change_event_count": 1,
"evidence_verified": true,
"exposure_regime_years": 1,
"id": "regime-02",
"obligation_class": "counsel-high",
"remediation_cost_log_squared_sum": 84.83036976765439,
"remediation_cost_log_sum": 9.210340371976184,
"remediation_cost_observation_count": 1,
"remediation_effort_log_squared_sum": 8.974411854812963,
"remediation_effort_log_sum": 2.995732273553991,
"remediation_effort_observation_count": 1
},
{
"change_event_count": 1,
"evidence_verified": true,Truncated for display — the full payload is 315 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.
{
"assumptions": [
"Complete zero-inclusive regime-year histories identify change frequency; mature positive remediation effort and cost identify lognormal work and cost; notice, capacity, reuse and finance loss are locally governed.",
"One common jurisdiction-change state is shared across represented obligation classes, and backlog explicitly arises when required remediation effort exceeds capacity available before the effective date."
],
"counts": {
"history_rows": 20,
"jurisdiction_groups": 1,
"obligation_classes": 1,
"scenarios": 2,
"simulations": 2000,
"supported_obligation_classes": 1
},
"decision": "review_ai_regulatory_change_capacity_and_tail",
"exposure_forecasts": [
{
"expected_change_events": 1.2885,
"expected_remediation_backlog_effort_days": 45.2526,
"expected_total_liability": 523803.5188,
"exposure_id": "high-risk-current",
"historical_change_events": 20,
"history_rows": 20,
"jurisdiction_group_id": "eu",
"obligation_class": "counsel-high",
"probability_remediation_late": 0.134,
"support_gate_pass": true
}
],
"forecast": {
"expected_change_events": 1.2885,
"expected_remediation_backlog_effort_days": 45.2526,
"expected_total_liability": 523803.5188,
"liability_conditional_value_at_risk": 3468378.2589,
"liability_value_at_risk": 2103453.9174,
"median_total_liability": 271432.5008,
"probability_any_remediation_backlog": 0.134,
"tail_probability": 0.05
},
"limitations": [
"The model forecasts a counsel-defined internal planning perimeter, not future law, regulatory interpretation, fines, enforcement action, compliance or permission to deploy.",
"Structural legal change, injunctions, product withdrawal, cross-jurisdiction conflict, omitted obligations and nonlinear shared-control work can make the represented tail incomplete."
],
"method": "bayesian_regulatory_change_compound_remediation_queue_liability_v1",
"reproducibility": {Truncated for display — the full payload is 54 lines.
How it works
Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.
- 1 Update each obligation class's Gamma-Poisson change-rate prior with complete zero-inclusive regime-year history and pool lognormal remediation effort and cost moments with governed priors.
- 2 Draw coherent scenarios and one shared change state per jurisdiction, then simulate affected systems, reusable work, notice, finite capacity, backlog, delay, disruption and enforcement loss.
- 3 Report expected liability, VaR/CVaR and class diagnostics only when row, event, effort, cost and evidence-support gates pass; otherwise abstain explicitly.
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.
- Historical intervals include zero-change periods; log moments use positive completed observations; notice, capacity, reuse, disruption and enforcement inputs share one horizon and currency basis.
- A predictive interval or risk estimate is not a deadline promise, causal explanation, or individual-performance judgment.
- This forecasts an internal counsel-defined planning perimeter, not future law, fines, regulatory interpretation, enforcement action or compliance.
Minimum evidence
- change_history: required and organization-defined
- current_obligation_exposures: required and organization-defined
- scenarios: required and organization-defined
How to validate it
Preserve the assignment/adoption design and validate overlap, pre-period or placebo diagnostics, attrition, interference policy, and cluster-level uncertainty; never tune on the estimated effect.
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
- versioned aggregate obligation-change panel joining only mature legal change events and completed finance/program outcomes while preserving common jurisdiction dependence
- obligation classes/regimes, exposure and event definitions, maturity, prior transport, support floors, jurisdiction groups, effort/cost basis, notice/capacity/reuse, finance loss, scenarios, horizon, tail appetite and model owner
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": "forecast counseldefined regulatorychange frequency lognormal remediation" }
→ finds "forecast_ai_regulatory_change_liability"
gitrevio_capability_describe
{ "capability_id": "forecast_ai_regulatory_change_liability" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "forecast_ai_regulatory_change_liability", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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