Towards Assessing Features of Dark Patterns in Cookie Consent Processes.

Abstract There has been a burst of discussions about how to characterize and recognize online dark patterns — i.e., web design strategies that aim to steer user choices towards what favours service providers or third parties like advertisers rather than what is in the best interest of users. Dark patterns are common in cookie banners where they are used to influence users to accept being tracked for more purposes than a data protection by default principle would dictate. Despite all the discussions, an objective, transparent, and verifiable assessment of dark patterns’ qualities is still missing. We contribute to bridging this gap by studying several cookie processes, in particular their multi-layered information flow —that we represent as message sequence charts—, and by identifying a list of observable and measurable features that we believe can help describing the presence of dark patterns in digital consent flows. We propose thirty one of such properties that can be operationalised into metrics and therefore into objective procedures for the detection of dark patterns.