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Part 3: Types of deceptive pattern

When I created darkpatterns.org in 2010, my objective was to spread awareness, so a large part of my focus was on branding and promotion. The names I chose were intended to be intriguing and memorable, so the world would find out about them. As a result, I didn’t create a rigorous classification system. It was more of a rallying cry to action.

If you review the literature on deceptive patterns today, you’ll find a somewhat bewildering array of different taxonomies and naming schemes. They’re all useful in one way or another, though the earlier work tends to be more primitive, and the later work tends to be more sophisticated owing to the larger body of evidence and knowledge available to draw on.

Different taxonomies typically have different objectives and tend to come from different areas of expertise. Behavioural economists and HCI researchers generally use psychological principles in their taxonomies. Mathur et al., for example, connect most of their deceptive design patterns with a specific cognitive bias. Similarly, Gray et al. (2018) based their work on a body of literature associated with UX and UI design, so their taxonomy had a strong UX/UI focus.1

More recently, deceptive patterns have become an area of interest for legal scholars, legislators and regulators. They tend to frame their taxonomies around the laws and legal terminology that’s relevant to their subject matter and their local jurisdiction. For example, the European Data Protection Board (EDPB) created a taxonomy that’s focused on privacy in social media platforms in the EU, drawing connections to the rules in the GDPR.2 This is useful if you happen to work in that area, but not quite so useful elsewhere.

The point here is that different taxonomies serve different purposes. You need to know what they are for before you critique them or use them in your work. If you’re seeking an overarching analysis of different taxonomies, you can take a look at the OECD’s 2022 ‘Dark Commercial Patterns’ report (Annex B)3 or Mathur, Mayer and Kshirsagar’s 2021 research paper, ‘What Makes a Dark Pattern… Dark?’4