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A liability estimate is a forward-looking, probabilistic dollar exposure number attached to a decision record. Where consequence links record what did happen — realised revenue, realised cost — liability estimates record what might happen if the decision turns out to have been wrong. They are derived, not ingested: the liability engine runs nightly and on-demand after every link creation to keep them current.
Liability estimates are probabilistic approximations for informational purposes only. They do not constitute legal, financial, or actuarial advice. Consult qualified professionals before acting on these numbers.

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How the number is calculated

Four factors multiply into the estimated exposure:
1

Base risk from the action type

Every action_type maps to a risk tier via a configurable matrix stored in your organisation settings. The defaults are:
action_typeTierBase exposure
email_sendlow$500
contract_generationmedium$10,000
pricing_changemedium$10,000
claim_denialhigh$50,000
medical_recommendationcritical$200,000
(anything else)medium$10,000
2

Confidence adjustment

Low-confidence decisions are riskier. A confidence value below 0.5 multiplies the base exposure by 2.0×.
3

Human-in-loop adjustment

A decision that was auto-executed without human review multiplies the exposure by 1.5×. Setting human_in_loop: true when logging the decision removes this multiplier.
4

Historical loss rate

For this agent and action pair, the fraction of past decisions that resulted in a negative consequence link (cost or liability type). Falls back to 0.5 when fewer than five historical decisions exist for the pair.
The formula: 
estimated_exposure = base × confidence_adj × hil_adj × max(loss_rate, 0.10)
The result is capped at liability_max_exposure (default $10 million) to prevent absurd numbers from a misconfigured matrix. A 90% confidence interval is constructed as [exposure × 0.5, exposure × 2.0] (log-symmetric).

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Shape

{
  "id": "lia_...",
  "decision_id": "d5e7a3b0-...",
  "estimated_exposure": "7500.00",
  "confidence_interval_low": "3750.00",
  "confidence_interval_high": "15000.00",
  "risk_category": "medium",
  "status": "active",
  "rationale": "action_type=pricing_change; tier=medium; base=$10,000; conf_adj=1.0x; hil_adj=1.5x; loss_rate=50.00%"
}
The rationale field gives you a human-readable breakdown of every factor that went into the number — useful for explaining an estimate to a colleague or an auditor.

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Reading estimates on the dashboard

The Liability tab shows each active estimate alongside its decision record, risk category badge, and confidence interval. Estimates are sortable by exposure amount, agent, and action type. Clicking through to a decision record shows you the full rationale and any consequence links that affected the calculation.

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Fetching aggregate exposure

# (Via the REST API — no dedicated SDK method yet.)
import httpx

r = httpx.get(
    "https://api.precipiq.dev/api/v1/analytics/liability",
    headers={"X-Precipiq-Key": "pq_test_demo_key_REPLACE_ME"},
    timeout=10.0,
)
r.raise_for_status()
agg = r.json()["data"]
print("total exposure:", agg["total_exposure"], agg["currency"])

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Alert thresholds

The dashboard subscribes to liability_estimate.updated events over the WebSocket stream. When an estimate’s exposure crosses liability_alert_threshold (default $100,000), a red toast fires:
Liability threshold exceeded — exposure•{exposure} •
Configure your threshold via Settings → Liability in the dashboard.
Lowering your threshold generates more alerts but gives you earlier warning on borderline decisions. Start with the default and tune it down once you have a baseline sense of your organisation’s typical exposure distribution.