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

TL;DR

This paper introduces a methodology for analyzing dark patterns over time within user journeys, using Amazon Prime's 'Iliad Flow' as a case study to understand their interplay and effects.

Contribution

It presents the first framework for temporal analysis of dark patterns, integrating static taxonomies with dynamic user journey insights and detection strategies.

Findings

Illustrates dark pattern interplay in Amazon's user journey

Proposes a methodology for temporal dark pattern analysis

Highlights implications for detection and user impact

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.