A Systematic Approach for A Reliable Detection of Deceptive Design Patterns Through Measurable HCI Features

Dark patterns are deceptive design elements of digital choice architectures that are implemented to drive users' actions towards decisions that are not necessarily in their best interest, such as accepting privacy-invasive practices. Most dark patterns are considered unlawful, but their description is rather informal. Thus, detecting dark patterns among the various existing design patterns and discerning what is an illegitimate design practice may depend on the subjective interpretation of expert users (such as regulators, civil society organizations, and academic researchers) who may not fully agree. The need to ground any evaluation on evidence calls for a reliable approach that is based on descriptions relying on observable, measurable features. Taking cookie consent as a use case, where dark patterns are ubiquitous and intensively under scrutiny, we propose a systematic approach to describe the characteristics of deceptive design patterns that are intended to reconcile the interpretations of expert users. In particular: i) we identify use case-specific dark pattern types using the ontology drafted by Gray et al. (2024); ii) we clarify the relationships between those types and the dark patterns' attributes proposed by Mathur et al. (2021); iii) we propose a list of observable and measurable user-interaction features of dark patterns covering visual, process, and language design aspects, iv) we describe the attributes based on our measurable features to lower the subjectivity of users' interpretation. Finally, we discuss our proposal's cross-domain applicability and the potential for future work, including how to improve the descriptions of the attributes via semiformal languages, to generate an objective and usable framework to assess the presence of deceptive design patterns in digital interfaces.