Getting Trapped in Amazon's "Iliad Flow": A Foundation for the Temporal Analysis of Dark Patterns

TL;DR

This work addresses the lack of temporal perspective in dark patterns research by analyzing Amazon Prime's Iliad Flow through a case study that grounds a Temporal Analysis of Dark Patterns (TADP) methodology. It introduces three levels of temporality—intra-page, inter-page, and system-level—and demonstrates how pattern co-occurrence and amplification unfold across a user journey, using the FTC complaint as evidence. The authors describe methods for identifying patterns, reconstructing interfaces, and mapping findings to a formal ontology, complemented by visualizations such as wireframes and service blueprints. The paper argues that TADP provides a robust, expert-driven framework to support regulation, legal proceedings, and scholarship by capturing temporal dynamics that static analyses miss.

Abstract

Dark patterns are ubiquitous in digital systems, impacting users throughout their journeys on many popular apps and websites. While substantial efforts from the research community in the last five years have led to consolidated taxonomies of dark patterns, including an emerging ontology, most applications of these descriptors have been focused on analysis of static images or as isolated pattern types. In this paper, we present a case study of Amazon Prime's "Iliad Flow" to illustrate the interplay of dark patterns across a user journey, grounded in insights from a US Federal Trade Commission complaint against the company. We use this case study to lay the groundwork for a methodology of Temporal Analysis of Dark Patterns (TADP), including considerations for characterization of individual dark patterns across a user journey, combinatorial effects of multiple dark patterns types, and implications for expert detection and automated detection.

Figures (4)

• Figure 1: An annotated wireframe of two of the pages in the Iliad Flow. Each annotation illustrates specific instances of high-level strategies (in bold) and meso-level dark patterns in the user interface. This figure includes reconstructed screenshots from the FTC complaint FTCAmazon2023-sr.
• Figure 2: A task flow indicating how three different pages of the Iliad Flow are related to one another and the overall user experience. Dotted lines with arrows indicate how a user navigates from one page to the next. Lines at the bottom indicate where dark patterns are present in the user experience over time, including both high-level strategies (in bold) and meso-level patterns. This figure includes reconstructed screenshots from the FTC complaint FTCAmazon2023-sr.
• Figure 3: A service blueprint outlining some of the actions related to Amazon Prime subscriptions as part of Amazon's larger ecosystem of products and services. Customer Actions (shown at the top) indicate goals that users might have, while Front Stage Actions (second row from the top) indicate how a user might engage with the system to support those goals. Back Stage Actions and Supporting Processes and Systems (bottom two rows) indicate how Amazon would support these goals on the back end in ways that are not visible to the end user. Dark patterns are indicated in shaded portions of the blueprint with specific high-level strategies in bold italics.
• Figure 4: This figure shows the levels of temporality and components for the Temporal Analysis of Dark Pattern Methodology.