Shadows in the Interface: A Comprehensive Study on Dark Patterns

As digital interfaces become increasingly prevalent, a series of ethical issues have surfaced, with dark patterns emerging as a key research focus. These manipulative design strategies are widely employed in User Interfaces (UI) with the primary aim of steering user behavior in favor of service providers, often at the expense of the users themselves. This paper aims to address three main challenges in the study of dark patterns: inconsistencies and incompleteness in classification, limitations of detection tools, and inadequacies in data comprehensiveness. In this paper, we introduce a comprehensive framework, called the Dark Pattern Analysis Framework (DPAF). Using this framework, we construct a comprehensive taxonomy of dark patterns, encompassing 64 types, each labeled with its impact on users and the likely scenarios in which it appears, validated through an industry survey. When assessing the capabilities of the detection tools and the completeness of the dataset, we find that of all dark patterns, the five detection tools can only identify 32, yielding a coverage rate of merely <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> mml:mn50</mml:mn> mml:mo%</mml:mo> </mml:math> . Although the four existing datasets collectively contain 5,566 instances, they cover only 32 of all types of dark patterns, also resulting in a total coverage rate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> mml:mn50</mml:mn> mml:mo%</mml:mo> </mml:math> . The results discussed above suggest that there is still significant room for advancement in the field of dark pattern detection. Through this research, we not only deepen our understanding of dark pattern classification and detection tools, but also offer valuable insights for future research and practice in this field.