"Automated Recognition of Dark Patterns in User Interfaces: Insights and Actionable Advice"


Hatched by naoya

Jul 09, 2024

2 min read


"Automated Recognition of Dark Patterns in User Interfaces: Insights and Actionable Advice"


Dark patterns in user interfaces (UI) refer to deceptive design elements that manipulate users into taking actions they may not intend to. Over the years, the research community has made significant progress in defining and categorizing different types of dark patterns. However, there is still a significant variation in how these patterns are implemented in software applications. This article explores the concept of automated recognition of dark patterns in UIs and provides insights into the challenges and potential solutions.


  • 1. Defining Dark Patterns: The term "dark patterns" implies the existence of powerful semantic signals that characterize different deceptive UI designs. However, the actual implementation of such designs can vary significantly. This makes the design of approaches to detect these patterns challenging.
  • 2. Lack of Unified Dataset: The research community currently lacks a large-scale dataset of dark patterns with finely localized information mapped to a unified classification system. AIDUI, an approach that analyzes UI text, icons, colors, and spatial characteristics, aims to automatically detect the presence of fundamental dark patterns.
  • 3. Visual and Textual Clues: AIDUI operates solely on visual data and requires only UI screenshots as input, making it easily scalable across multiple software domains. The underlying idea of AIDUI is that there are several visual and textual clues that, when appearing together, indicate the presence of various dark patterns.

Actionable Advice:

  • 1. Implement Visual Cue Detection: Use deep learning-based object detection models to identify UI objects representing visual cues of dark patterns. Extract UI segments containing both text and non-text content for further analysis.
  • 2. Utilize Text Analysis Techniques: Apply text pattern matching, color analysis, and spatial analysis techniques to the extracted UI segments to identify potential sets of dark patterns. This analysis phase plays a crucial role in identifying the underlying dark patterns.
  • 3. Predict Final Set of Dark Patterns: Combine the results from visual cue detection and DP analysis phases to predict the final set of fundamental dark patterns within a given UI. This step involves resolving dark patterns at both segment and UI levels.


Automated recognition of dark patterns in user interfaces is a challenging task that requires the integration of various analysis techniques. The AIDUI approach, which leverages visual and textual clues, shows promising results in detecting and localizing dark patterns. However, there are areas for future research, such as automating the detection of other categories of dark patterns and addressing the limitations of the current text analysis techniques. By continuously refining and improving these automated recognition methods, we can make user interfaces more transparent and user-friendly.

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