Automated Detection and Classification of Dark Patterns in User Interfaces: Core Principles, Methodologies, and Contemporary Applications

Author

Christy Shibu, Prof. Riya Prakash, Dr. L. C. Manikandan


Abstract

 For many years, user interface design centered almost entirely on creating clear, user-friendly, and predictable experiences. Today, however, many online services embed sneaky design tricks—often called "dark patterns"—that trick users by tapping into natural cognitive biases. These tactics push people into doing things they did not intend, such as making unintended purchases, signing up for recurring billing, or sharing sensitive personal data without knowing it. Because e-commerce platforms and mobile applications are so massive, checking these interfaces manually is no longer practical.

Automated Detection and Classification of Dark Patterns (ADCDP) offers a practical solution to these issues by scanning interfaces in real time right on the client’s device. Instead of relying on occasional manual audits, ADCDP continuously inspects individual interface elements using machine learning, natural language processing (NLP), and computer vision. This survey breaks down how ADCDP works under the hood, exploring dynamic Document Object Model (DOM) extraction, transformer-based text analysis, optical character recognition (OCR), visual hierarchy scoring, and live browser interventions. We also review real-world applications across e-commerce checkouts, mobile software, social platforms, and privacy compliance tools. Finally, we discuss remaining hurdles, including processing speed, edge device limitations, regulatory standards, and future avenues for privacy-focused defense.



Keywords

Dark Patterns, User Interface (UI), Machine Learning, Transformer Models, Natural Language Processing, Privacy Enforcement, Automated Systems, Computer Vision.



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