File size: 1,771 Bytes
fcf3342 25ff8fa fcf3342 65a7c06 fcf3342 25ff8fa 65a7c06 25ff8fa 65a7c06 25ff8fa 65a7c06 25ff8fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ---
title: Dark Pattern Detector
emoji: 🛡️
colorFrom: purple
colorTo: red
sdk: static
pinned: true
tags:
- dark-patterns
- nlp
- consumer-protection
- distilbert
---
# Dark Pattern Detector 🛡️
An interactive NLP classification engine designed to detect deceptive UI copy, artificial scarcity, and manipulative e-commerce patterns.
- **GitHub Repository**: [JayanGupta/Dark-Pattern-Detection](https://github.com/JayanGupta/Dark-Pattern-Detection)
- **Author**: [Jayan Gupta](https://huggingface.co/Jayan12321)
---
## 🎯 What are Dark Patterns?
Dark patterns are user interface designs crafted to trick users into doing things they might not otherwise do (e.g. buying unwanted add-ons, signing up for disguised recurring subscriptions, or agreeing to invasive tracking).
---
## 📋 Detected Taxonomies
This tool identifies manipulative patterns across industry-standard taxonomies:
1. **Urgency & Scarcity**: Artificial countdown timers and fake low-stock alerts.
2. **Misdirection**: Using visual hierarchy or pre-checked checkboxes to steer users toward a specific choice.
3. **Social Proof Exploitation**: Synthetic activity notifications ("34 people looking at this right now").
4. **Forced Continuity**: Easy one-click signups combined with labyrinthine cancellation flows.
5. **Confirm-shaming**: Emotionally manipulative copy on opt-out buttons ("No thanks, I hate saving money").
6. **Hidden Costs**: Unannounced fees revealed only on the final checkout screen.
---
## 🛠️ Development & Transparency Note
Designed and developed by **Jayan Gupta**. The taxonomy and heuristics are based on FTC consumer protection guidelines and academic dark-pattern datasets. AI assistants were used for prototyping and documentation structuring.
|