Features. An option is to identify a set of discerning features of the interface and record the values of those features. This option is the one we used in this paper. A supervised learning algorithm can be used to build a prediction model essentially finding a pattern in the feature values that classifies an interface as either containing or not dark patterns and identifying which one. Advantage. Building on the work of Soe et al. (2020), a large selection of discerning features is used to describe the properties of online cookie banners from news outlets. The advantage of this approach over the other two is that interactive phenomena, such as invoking several interfaces, can be described and both visual and textual cues can be captured in the representation. In Section 3 we describe how the features from the Soe et al. (2020) dataset were processed before being used to train a prediction model. Limitation. The clear disadvantage is that some feature values will be difficult to be automatically "harvested" and virtually all feature values will require different AI post-processing techniques. Namely, after one algorithm (or a human) has identified and scraped the text, another is needed to analyse it. For example, text would require sentiment analysis, and the identification of widgets used would require a image processing. Instead, an ideal approach, and one open problem for future work consists in identifying the features whose values can be automatically scraped and post-processed and which are most relevant to distinguish a dark pattern. The more precise the definition of dark patterns, the easier it is to identify the relevant features. A separate limitation of the features approach is the challenge of humans being able to correctly label the **data** points with the right dark pattern. We discuss this issue next.