Why is the User Interface a Dark Pattern? : Explainable Auto-Detection
  and its Analysis
Yuki Yada Tsuneo Matsumoto Fuyuko Kido Hayato Yamana
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.