Automated decisions affecting consumers explained with opt-out
Why it matters
The FTC has flagged fully automated consequential decisions without explanation or recourse as unfair practices under FTC Act §5, and GDPR Article 22 creates a legal right for EU users to human review of automated decisions with significant effects. Algorithmic pricing that shows different prices to different users without disclosure, automated content moderation with no appeal path, and behavioral scoring that assigns service tiers without explanation each expose consumers to material harms they have no mechanism to identify or contest. The combination of FTC unfairness doctrine and GDPR Article 22 creates dual-jurisdiction liability for products with EU users.
Severity rationale
Low because automated decision harms require the consumer to experience an adverse outcome and be unable to identify its source — multi-step harm that is real but not immediate — though the FTC's increasing focus on algorithmic systems raises the trajectory of enforcement risk.
Remediation
Disclose automated decision systems in the privacy policy and provide a recourse path for consequential decisions.
// Show users why they see what they see
function RecommendationExplanation({ reason }: { reason: string }) {
return (
<p className="text-xs text-gray-500">
Recommended because: {reason}{' — '}
<button className="underline" onClick={() => showPreferenceSettings()}>
adjust your preferences
</button>
</p>
)
}
Add to src/app/privacy/page.tsx or equivalent:
## Automated Decision-Making
We use automated systems to [personalize recommendations / assign
service tiers / moderate content]. These systems use [your usage
patterns / your account type / reported content].
If you believe an automated decision affecting your account is
incorrect, contact support@example.com. We will review the decision
within [X business days].
For dynamic pricing: disclose in the privacy policy that prices may vary based on account or usage signals. GDPR Article 22 requires an opt-out or human review path for decisions with 'significant effects' on EU users.
DetectionPro
The detection method for this pattern is part of AuditBuffet Pro.
The full method (what to look for, pass and fail conditions, and how results get reported) ships inside the FTC Consumer Protection Audit. The pattern definition, severity rationale, and remediation remain openly licensed under CC-BY-4.0.
External references
- external · ftc-act-section-5 — FTC Act Section 5 — Unfair or Deceptive Acts or Practices
- external · ftc-ai-guidance-2024 — FTC Guidance on AI and Automated Decision-Making (2024)
- gdpr · Art. 22 — GDPR Article 22 — Automated individual decision-making
Taxons
History
- 2026-04-18·v1.0.0·Initial import from ftc-consumer-protection·automated