Retail AI Incident Card Profile v0.1 draft. A Kinetic Gain Protocol Suite AI Incident Card profile for retailers / marketplaces / brands documenting AI failure incidents — pricing discrimination complaints, recommendation bias surfaces, fraud false-positive harm, biometric misidentification, refund discrimination, ad-personalization complaints, and dark-patterns enforcement events.
Part of the Kinetic Gain Protocol Suite RetailTech 6-pack.
Status: v0.1 draft. Canonical example incident card at
examples/parkway-pricing-discrimination-incident.json.
When a retail AI decision causes consumer harm — a complaint to state AG, an FTC investigation letter, a biometric misidentification at a self-checkout kiosk, a refund-decision discrimination claim — the retailer needs to document the incident in a defensible, comparable format. The AI Incident Card provides that shape; this profile narrows the event-type taxonomy + reason-code taxonomy to RetailTech.
| Event type | Trigger |
|---|---|
retail.pricing.discrimination-complaint |
Consumer alleges algorithmic price varied by protected-class proxy |
retail.recommendation.bias-surface-complaint |
Consumer alleges ranking systematically excluded a protected class |
retail.loyalty.tier-discrimination-complaint |
Consumer alleges loyalty-tier advancement biased by protected-class proxy |
retail.fraud.false-positive-harm |
Fraud flag erroneously blocked legitimate transaction causing harm |
retail.biometric.misidentification-incident |
Face/voice/palm match failed or matched wrong shopper |
retail.biometric.consent-violation |
BIPA §15(b) consent gap, retention overrun, or sharing violation |
retail.refund.discrimination-claim |
Refund / return decision pattern correlates with protected class |
retail.ad-personalization.harm-complaint |
Targeted ad caused harm (medical info inferred, financial vulnerability targeted) |
retail.dark-patterns.enforcement-event |
FTC / state AG / NAD enforcement letter or complaint over dark patterns |
retail.deceptive-ai-output.complaint |
AI-generated content (chatbot, review, description) misled consumer |
retail.minor-targeting.violation |
Underage shopper targeted with age-restricted content / personalization |
retail.data-broker.exposure-incident |
Personal data appeared in a data broker or breach with retailer attribution |
| Reason code | What it means |
|---|---|
proxy-feature-overweighted |
A model feature ended up acting as a protected-class proxy |
training-data-skew |
Training data underrepresented the harmed cohort |
vendor-model-substitution |
Vendor swapped the model and behavior changed |
disclosure-gap |
Personalized-pricing disclosure was missing or unclear |
consent-flow-failure |
Opt-out flow failed silently (CCPA / CPRA / ADMT) |
biometric-template-collision |
False match between two similar biometric templates |
human-review-bypass |
Decision auto-issued where human review was required |
cross-state-misapplication |
State-specific obligation not applied to a covered transaction |
| Band | Trigger | Action |
|---|---|---|
informational |
No regulator engagement, no consumer harm | Log + monthly aggregate review |
monitor |
Consumer complaint, no enforcement | Log + 90-day pattern monitoring |
escalate |
Regulator inquiry letter, single incident, no widespread harm | Engage GC + Trust + write public note if facts warrant |
crisis |
Multi-jurisdiction enforcement OR class-action posture OR widespread harm | Stop the decision flow + outside counsel + board notification |
RetailTech-readiness scaffolding for AI Incident Card documentation aligned in vocabulary with FTC Section 5 complaint handling, state AG inquiry response, EU AI Act Art 73 (serious incident reporting where applicable). The profile provides the shape but does not by itself establish compliance with any framework. Per the standing public-language guardrail: readiness · evidence · posture · controls · scaffolding.
| Repo | Role |
|---|---|
retail-decision-record-audit-stream |
Source events to reconstruct what happened |
consumer-pricing-bias-coverage-lab |
Cohort analysis to confirm or refute alleged bias |
ftc-algorithmic-pricing-readiness-evidence-bundle |
Incident log section accumulates these cards |
shopper-pii-vault-contract-profile |
Resource-access proof for any data implicated |
financial-ai-incident-card-profile |
Sibling FinTech incident-card profile |
MIT — see LICENSE.