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Past studies have illustrated the prevalence of UI dark patterns, or userinterfaces that can lead end-users toward (unknowingly) taking actions thatthey may not have intended. Such deceptive UI designs can result in adverseeffects on end users, such as oversharing personal information or financialloss. While significant research progress has been made toward the developmentof dark pattern taxonomies, developers and users currently lack guidance tohelp recognize, avoid, and navigate these often subtle design motifs. However,automated recognition of dark patterns is a challenging task, as theinstantiation of a single type of pattern can take many forms, leading tosignificant variability.  In this paper, we take the first step toward understanding the extent towhich common UI dark patterns can be automatically recognized in modernsoftware applications. To do this, we introduce AidUI, a novel automatedapproach that uses computer vision and natural language processing techniquesto recognize a set of visual and textual cues in application screenshots thatsignify the presence of ten unique UI dark patterns, allowing for theirdetection, classification, and localization. To evaluate our approach, we haveconstructed ContextDP, the current largest dataset of fully-localized UI darkpatterns that spans 175 mobile and 83 web UI screenshots containing 301 darkpattern instances. The results of our evaluation illustrate that \AidUIachieves an overall precision of 0.66, recall of 0.67, F1-score of 0.65 indetecting dark pattern instances, reports few false positives, and is able tolocalize detected patterns with an IoU score of ~0.84. Furthermore, asignificant subset of our studied dark patterns can be detected quite reliably(F1 score of over 0.82), and future research directions may allow for improveddetection of additional patterns.

AidUI: Toward Automated Recognition of Dark Patterns in User Interfaces