Unfair Discrimination AI Incident Card Profile v0.1 draft. Profile of the AI Incident Card spec scoped to insurance unfair-discrimination, biased decisioning, NAIC AI Model Bulletin governance gaps, ECDIS input defects, FCRA-dispute-pattern AI events, and state DOI exam findings. Provides the severity / event_type / regulator-referral taxonomy a state DOI market-conduct examiner + Fair Insurance Committee + actuarial committee + outside counsel expect when an AI insurance tool is implicated in a consumer harm.
Part of the Kinetic Gain Protocol Suite.
Status: v0.1 draft. Profile at
profile.json, canonical example atexamples/coastguard-aian-rural-incident.json.
When an AI insurance tool is implicated in a consumer harm — a patterned four-fifths violation, an ECDIS defect, an FCRA dispute pattern, an actuarial-soundness gap, a third-party vendor control failure, or a state DOI exam finding — the insurer's compliance / actuarial / outside-counsel / Fair Insurance Committee chain needs a single canonical record covering: what happened, who was affected, what AI system was involved, what state's regulators have jurisdiction, what referral pathways apply, what's the remediation plan, and is it signed. State DOI examiners triangulate from this record to the underlying audit-stream events, bias-coverage bundle, vendor due-diligence package, and Decision Card.
This profile defines that record's shape — the InsurTech sibling of the medical-adverse-event-incident-card (HealthTech), ai-student-record-incident-card-profile (EdTech), and title-chain-evidence-incident-card-profile (PropTech).
| Code | Description |
|---|---|
ai-unfair-discrimination-pattern |
Pattern disproportionately disadvantaging a protected class (CO SB 21-169 / NAIC §3 implicating) |
ai-individual-adverse-action-error |
Single AI-recommended adverse action overturned, indicating model error |
ai-ecdis-input-defect |
External Consumer Data and Information Source defect (NY DFS CL 7 implicating) |
ai-fcra-dispute-pattern |
FCRA dispute pattern traced to AI tool reliance on contested consumer report |
ai-actuarial-soundness-defect |
AI rating/pricing model output lacks documented actuarial soundness |
ai-third-party-vendor-control-failure |
Third-party AI vendor missed contractual control |
ai-governance-program-gap |
Internal AI governance program gap |
ai-consumer-notice-defect |
AI-related consumer notice defect |
ai-data-breach-with-ai-component |
Data breach with AI training-data or model-output component |
ai-state-doi-exam-finding |
State DOI market-conduct exam finding implicating AI |
| Code | Description |
|---|---|
| S1-low | Single-consumer notice-only; remediated within 30 days; no regulator notification |
| S2-moderate | Pattern < 100 consumers OR single material; state DOI notification recommended |
| S3-serious | Pattern 100–1000 OR four-fifths violation OR ECDIS defect; state DOI notification REQUIRED |
| S4-critical | Pattern > 1000 OR cross-state OR systemic four-fifths OR CO §10-3-1104.9 referral; multi-state DOI + CFPB + DOJ evaluation REQUIRED |
state-doi-notification · naic-mcas-flag · cfpb-fcra-referral · doj-civil-rights-referral · state-ag-civil-rights-referral · state-doi-bulletin-violation-filing
Each incident records the evaluation status (filed / scheduled / evaluated-not-applicable / evaluated-not-required) per pathway, with destinations + timestamps + reasons.
examples/coastguard-aian-rural-incident.json — Coastguard Insurance's S3-serious unfair-discrimination pattern: VendorI ClaimsTriage v3.x 0.61 selection-rate ratio for AIAN-claimant rural property claims, 47 consumers affected across CT/NH/VT/RI, four state DOI notifications filed, NAIC MCAS scheduled, CFPB / DOJ / state-AG pathways evaluated and recorded as not-applicable / not-required with reasons. Cross-references the insurance-decision-record-audit-stream sibling (linked audit events) and the insurance-applicant-bias-coverage-lab sibling (linked bias coverage bundle).
| Repo | Role |
|---|---|
insurance-decision-record-audit-stream |
Source of linked_audit_stream_event_ids |
insurance-applicant-bias-coverage-lab |
Source of linked_bias_coverage_bundle_id |
naic-ai-bulletin-readiness-evidence-bundle |
Broader readiness bundle this incident becomes a part of |
state-insurance-ai-disclosure-tracker |
Determines which state's bulletin-violation-filing pathway applies |
medical-adverse-event-incident-card |
Sibling HealthTech Incident Card |
ai-student-record-incident-card-profile |
Sibling EdTech Incident Card |
title-chain-evidence-incident-card-profile |
Sibling PropTech Incident Card |
InsurTech-readiness scaffolding for the Incident Card record an insurer maintains for AI-implicating consumer harm events. Supports an insurer's program toward NAIC AI Model Bulletin §3 incident-response readiness, state DOI notification readiness, NAIC MCAS supplementary-disclosure readiness, CO SB 21-169 §10-3-1104.9 referral pathway readiness, and CFPB / DOJ / state-AG referral evaluation discipline. Does not by itself establish compliance with any state DOI bulletin or any federal regulator. Per the standing public-language guardrail: readiness · evidence · posture · controls · scaffolding — never "incident-response-compliant" or "regulator-attested" without an external attestation.
MIT — see LICENSE.