To detect deceptive design patterns on UIs, traditional artificial intelligence models, such as machine learning, have limited coverage and a lack of multimodality. In contrast, the capabilities of Multimodal Large Language Model (MM-LLM) can achieve wider coverage with superior performance in the detection, while providing reasoning behind each decision. We propose and implement an MM-LLM-based approach (DeceptiLens) that analyzes UIs and assesses the presence of deceptive design patterns. We utilize Retrieval Augmented Generation (RAG) process in our design and task the model with capturing the deceptive patterns, classifying its category, e.g., false hierarchy, confirmshaming, etc., and explaining the reasoning behind the classifications by employing recent prompt engineering techniques, such as Chain-of-Thought (CoT).
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DeceptiLens: an Approach supporting Transparency in Deceptive Pattern Detection based on a Multimodal Large Language Model
An MM-LLM-based approach that analyzes UIs and assesses the presence of deceptive design patterns and is capable of capturing the deceptive patterns in UIs with high accuracy while providing clear, correct, complete, and verifiable justifications for its decisions.