| --- |
| title: Dark Pattern Detector |
| emoji: 🛡️ |
| colorFrom: purple |
| colorTo: red |
| sdk: static |
| pinned: true |
| tags: |
| - dark-patterns |
| - nlp |
| - consumer-protection |
| - distilbert |
| --- |
| |
| # Dark Pattern Detector 🛡️ |
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| An interactive NLP classification engine designed to detect deceptive UI copy, artificial scarcity, and manipulative e-commerce patterns. |
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| - **GitHub Repository**: [JayanGupta/Dark-Pattern-Detection](https://github.com/JayanGupta/Dark-Pattern-Detection) |
| - **Author**: [Jayan Gupta](https://huggingface.co/Jayan12321) |
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| ## 🎯 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). |
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| ## 📋 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. |
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| ## 🛠️ 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. |
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