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In the digital context, there are increasingly points at which users encounter data protection topics, especially to provide consent. At the same time, companies are heavily involved in handling data in a legally compliant manner and obtaining permission to process data. Entire industries have built up around “managing” user consent. However, a significant part of the added value of such solutions is to promise a high consent rate: That is, to apply designs that are lawful, but still nudge users to disclose data as much as possible. This does not have to happen through deceptive design [18, 37, 62](see also the chapter “The Hows and Whys of Dark Patterns: Categorizations and Privacy”) but can also work by achieving transparency in terms of privacy and security safeguards (low risks) and presentation of added value (large benefit)[24, 51]. Nevertheless, it is usually done in the interest of the data processor that in turn can be to the detriment of a free decision by the customer aka data subject. The data-driven economy fosters exactly such unbalanced relations between the entities that gather and process personal information and the individuals who are often unaware of the extent and the significance of the processing [20]. By large, there are three ways of influencing said imbalance: First, by the increasing business incentive for companies to collect and make use of (especially: personal) data, actors become more likely to engage in more excessive data collection practices. Second, by the increasing complexity and opaqueness of algorithms used, it is becoming more difficult to explain data processing, especially to non-tech-