Leveraging interdisciplinary methods for evidence collection in enforcement

“Dark patterns” are manipulative, deceptive design practices deployed in online services to influence users’ decisions towards undesired or negative outcomes. Interdisciplinary by nature, dark patterns implicate concepts of autonomy and choice from law, human behaviour from the psychology and social science disciplines, and design and human-computer interaction (HCI) from technical fields and industry. A body of enforcement actions and regulatory fines worldwide as discussed within this article comprise a growing effort to minimise the impact of dark patterns. However, despite this regulatory momentum, it remains unknown to what extent scientific research methods and evidence types may influence regulatory decisions, which is relevant for effective evidence-based enforcement.As such, dark patterns present a case study for reflecting upon narrowing the academic-enforcement divide. Our team spans design, HCI, computer science, and law, and examines investigatory methodologies towards insight for strengthening collaboration between scholars and regulators. This interdisciplinary work considers investigatory methods from both academia and industry, then as inferred from dark patterns enforcement cases to relate methods used by both groups. We discuss challenges and opportunities for tightening the gap between researchers and regulators, and propose suggestions for both scholars and enforcers to tighten feedback loops. We additionally highlight informal investigation methods as an opportunity to strengthen collaboration

Autonomy · Behavioural sciences · Best practice · Enforcement · Great Rift · Law enforcement · Social network analysis · Data Visualization and Analytics · Ethics and Social Impacts of AI · Privacy, Security, and Data Protection