Dark Patterns Reconsidered: A Cross-Taxonomic and Conceptual Mapping for Ethical Interface Design - Meirezaldi, Onni (Sekolah Tinggi Ilmu Manajemen YKPN Yogyakarta, 2025)
Dark patterns, manipulative interface strategies that steer users toward actions contrary to their interests, have become ingrained in commerce, social media, and data‑collection flows. Research and regulation still lack a shared vocabulary for identifying and addressing them. This study aims to close that gap by proposing a comprehensive conceptual definition and a reconciled taxonomic map that clarifies how dark patterns operate and why they negatively impact users. Based on a directed literature review of 54 peer‑reviewed sources indexed in Scopus, the analysis identifies four foundational elements: manipulative intent, information asymmetry, constrained choice, and exploitation of cognitive bias. It combines them into a single definition that unites legal, psychological, and HCI perspectives. It then cross‑compares the leading taxonomies of Brignull et al., Grey et al., Mathur et al., and Zagal et al., demonstrating agreement on five mechanism families, namely Obstruction, Sneaking, Interface Interference, Forced Action, Nagging, while highlighting differing focuses on functional harms. To address this issue, the article introduces a two‑dimensional grid that overlays those mechanisms with four functional areas: Finance, Privacy, Time Capture, and Psychological Pressure, creating a flexible framework capable of classifying both traditional and emerging dark‑pattern strategies. The resulting model offers scholars a stable analytical framework for theory building, supplies regulators with enforceable categories for consumer protection, and equips designers with a diagnostic tool for auditing interface ethics. The study establishes a conceptual foundation for future empirical measurement, automated detection, and evidence‑based policy to foster a more transparent and autonomy‑respecting digital ecosystem by unifying disparate definitions and rationalizing taxonomies.