Dark Patterns May Be The Blind Spot in Your AI Governance Playbook

by: Dhara Shah

Dark patterns are not a novel concept – in fact we have covered this topic before in relation to the FTC’s report on “Bringing Dark Patterns to Light.” However, how much have we considered dark patterns in this new world of AI?

With the increased use of AI comes the increased chance of it bringing with it dark patterns – often ones we may not spot as easily.

Let’s Level-Set: What Are Dark Patterns?

“Dark patterns” are UI designs that steer, subvert, or impair a user’s decision-making by pushing them to choices they wouldn’t freely make. Think hidden opt‑outs, false urgency, or guilt‑trip copy. This can show up in a myriad of ways, some common examples include:

  • Making the “accept” button large and keeping the “decline” button small. Effectively weighing the choice architecture in favor of consent.

  • Language that says “No, I don’t care about endangered species” next to the reject button to shame a user to take a certain choice.

  • Countdown timers (“Offer ends in 00:09 minutes!”) or fake low‑stock notices push users to act before they can reflect.

How AI Turns Dark Patterns Darker

AI supercharges these tactics in various ways, including:

  • Learning: Reinforcement‑learning agents A/B‑test in real time, discovering the micro‑phrases that spike conversion.

  • Personalizing: AI models craft bespoke copy and imagery, calibrating the pitch to each user’s profile and mood.

  • Predicting: Deep models forecast the precise moment a user is most susceptible, then trigger the “Only one seat left!” concept.

The result? Dark patterns move from static traps to adaptive, ever‑evolving persuasion engines.

Mitigation: Four Practical Safeguards

Dark patterns can result in penalties and fines from both state regulators and the FTC. To help offset the chance of being subject to enforcement action, you can start by implementing the following steps into your AI user guide:

1.     Pre‑launch stress‑tests. Scan every model release for confirm‑shaming language, fabricated scarcity, or manipulative emotion.

2.     Granular logging. Log which UI variant each user sees; those audit trails are gold when enforcement knocks.

3.     Proper review. Review AI‑generated marketing claims with the same rigor you apply to human copy.

4.     AI use tracking. Map and review where AI is generating UI, and review these for dark patterns just like you would if a human created it.

Originally published by InfoLawGroup LLP. If you would like to receive regular emails from us, in which we share updates and our take on current legal news, please subscribe to InfoLawGroup’s Insights HERE.

Dhara ShahAI, Artificial Intelligence, Dark Patterns