Why is the User Interface a Dark Pattern? : Explainable Auto-Detection and its Analysis
This work addresses the issue of dark patterns for online service users, offering an incremental improvement through explainable auto-detection.
The paper tackled the problem of automatically detecting deceptive user interface designs (dark patterns) in e-commerce by training a transformer-based model on text data and applying post-hoc explanation techniques like LIME and SHAP to identify influential terms, achieving interpretable detection without specifying concrete performance numbers.
Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. Dark patterns, such as privacy invasion, financial loss, and emotional distress, can harm users. These issues have been the subject of considerable debate in recent years. In this paper, we study interpretable dark pattern auto-detection, that is, why a particular user interface is detected as having dark patterns. First, we trained a model using transformer-based pre-trained language models, BERT, on a text-based dataset for the automatic detection of dark patterns in e-commerce. Then, we applied post-hoc explanation techniques, including local interpretable model agnostic explanation (LIME) and Shapley additive explanations (SHAP), to the trained model, which revealed which terms influence each prediction as a dark pattern. In addition, we extracted and analyzed terms that affected the dark patterns. Our findings may prevent users from being manipulated by dark patterns, and aid in the construction of more equitable internet services. Our code is available at https://github.com/yamanalab/why-darkpattern.