As vibe coding and LLM-driven development lower the barrier to building functional interfaces, the distance between an idea and a deployed product has never been shorter. However, this speed often comes at a hidden cost: the inadvertent scaling of deceptive patterns. Essentially, your AI-generated UI is shipping deceptive behavior you didn't explicitly code, and you can still be held responsible for it. This begs the question: When we prompt our way to a UI, what are the consequences of the deceptive UX debt of the training data we are inheriting?
In this session, we dive into the murky waters of deceptive UX, from classic "roach motels" – a UX pattern where it is incredibly easy to sign up for a service but intentionally difficult or hidden to cancel– to the new era of AI-driven prototyping, where personalized algorithms can exploit cognitive biases at an individual scale.

Talk structure:
In this talk, we'll walk through a live, demo-driven narrative we are calling "The Evolution of Deceptive UX Patterns," structured in three phases:

Phase 1: The Magic
We open with a live (or pre-recorded) vibe-coding demo, prompting Cursor using Claude Opus 4.6 to generate a polished, high-conversion mobile ordering UI for a taco truck in seconds. The result looks stunning and feels like the future.
- What are the pros of vibe coding this fast? What things did Claude get right here?

Phase 2: The Hidden Tax
We re-examine the "Magic UI" from Phase 1 using a "magnifying glass" effect to look under the hood, specifically at deceptive patterns.
- Confirm-shaming example
- Harms: emotional manipulation, erosion of trust
- Nudging example
- Harms: loss of autonomy/control, capitalizing on users' impulses
- Data tracking/surveillance framed in a positive way example
- Harms: loss of autonomy/control, emotional manipulation
- The truth: This isn't a glitch; it's an inheritance. AI doesn't "choose" to be deceptive, it mirrors the deceptive patterns ubiquitous in its training data
- Why is it ubiquitous in training data you might ask?
- Well, when was the last time you saw a “Buy” button that was a significantly different color than the rest of the buttons on the screen?
- Show examples from Amazon, Etsy, online shopping sites
- When was the last time you were confirmed-shamed?
- Show a bunch of examples on the presentation
- By vibe coding without checks and filters, we're effectively scaling a legacy of UIs that have deceptive patterns embedded into them

Phase 3: The New Toolkit
We show how to use the same AI tools that build these interfaces to identify, flag, and neutralize deceptive patterns before they reach the user.
- Demo: Using an AI Agent (like Cursor) as an ethical auditor
- The takeaway: A 3-step "Sanity Check" framework for auditing AI-generated interfaces.

What attendees will gain from this talk:
Attendees will walk away with a framework for ethical prompting and a toolkit for auditing AI-generated interfaces, ensuring that the future of development is as transparent as it is fast. Key takeaways include:
- Ability to identify modern deceptive patterns that specifically leverage AI and machine learning
- Understand the ethical implications of using "vibe coding" (natural language programming) in production environments
- How to use AI, particularly Cursor skills, to implement automated and manual "sanity checks" to ensure AI-generated UIs remain user-centric and honest

Why this talk:
DevConf is about the intersection of craft and technology. As designers and developers alike move toward becoming "orchestrators" of AI-generated code, understanding the ethics of the software and interfaces we ship is not just a designer's job, it's a core engineering responsibility too.