AI deceptive design pattern detector
AI-powered flows are fertile ground for dark patterns: a model that “personalizes” an upsell can quietly manufacture urgency, bundle consent, or shame users out of opting in. Regulators have caught up — the EU Digital Services Act (Art. 25), the Unfair Commercial Practices Directive, the UK CMA’s guidance, and the US FTC all now target deceptive design. This detector reads your flow description and flags wording associated with known dark patterns, mapped to the regime you select.
How it works
You describe the flow in plain language, including the exact button labels, defaults, and any countdowns or scarcity claims. The detector runs a library of pattern matchers grouped by dark-pattern type — confirmshaming, manufactured urgency, hidden subscription / forced continuity, consent bundling, pre-ticked boxes, hidden costs, and trick wording. Each match shows the phrase that triggered it, why it is risky, the citation for your chosen jurisdiction, and a compliant redesign. Everything runs locally; nothing is uploaded.
Notes and limits
- Describe defaults explicitly. Many dark patterns live in defaults (pre-ticked, auto-renew, opt-out) — say what is selected before the user acts.
- Pattern matching is a first pass. It catches common wording but cannot see layout tricks like tiny decline links or color-weighted buttons; review those visually too.
- Symmetry is the fix. The cleanest compliant pattern is a symmetric choice: equally prominent accept and decline, neutral labels, no pre-selected option.
- Confirm with counsel. Use the flags to prioritize, then get a qualified legal review before you rely on a compliance conclusion.