The Dark (Patterns) Side of UX Design

By Colin M. Gray, Yubo Kou, Bryan Battles, Joseph Hoggatt, Austin Toombs. International Conference on Human Factors in Computing Systems, 2018

858 citations recorded by the source corpus.

Interest in critical scholarship that engages with the complexity of user experience (UX) practice is rapidly expanding, yet the vocabulary for describing and assessing criticality in practice is currently lacking. In this paper, we outline and explore the limits of a specific ethical phenomenon known as "dark patterns," where user value is supplanted in favor of shareholder value. We assembled a corpus of examples of practitioner-identified dark patterns and performed a content analysis to determine the ethical concerns contained in these examples. This analysis revealed a wide range of ethical issues raised by practitioners that were frequently conflated under the umbrella term of dark patterns, while also underscoring a shared concern that UX designers could easily become complicit in manipulative or unreasonably persuasive practices. We conclude with implications for the education and practice of UX designers, and a proposal for broadening research on the ethics of user experience.

Subfields: Cognitive Neuroscience, Safety Research, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/ddb4a6538bbbb7468ff949621d2185693e5ebae5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Eric D. Ragan
2. Niloofar Zarei
3. Sina Mohseni
4. Michael Veale
5. Sunipa Dev
6. Adina Williams

In Safety Research

1. Lalana Kagal
2. David R Karger
3. Eric D. Ragan
4. Niloofar Zarei
5. Sina Mohseni
6. Michael Veale
7. Arvind Narayanan
8. Sunipa Dev

In Human-Computer Interaction

1. Eric D. Ragan
2. Niloofar Zarei
3. Sina Mohseni

Citations in the corpus

• Dark Patterns at Scale (2019, methodology): “Our data set contains several new instances and variations of previously documented dark patterns [32, 48].”
• Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence (2020, background): “Interface designs that try to guide end-users into desired behaviour through malicious interaction flows are referred to as “dark patterns” [29].”
• A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “tion and verbalization to communicate or summarize key aspects of a model. From a different perspective, Chromik et al. [36] extends the idea of “dark patterns” from interactive user interface design [68] into machine learning explanations. They review possible ACM Trans. Interact. Intell. Syst., Vo”
• UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception (2020, methodology): “, [45] proposed five different types of Dark Patterns:”
• What Makes a Dark Pattern... Dark?: Design Attributes, Normative Considerations, and Measurement Methods (2021, background): “But there is nothing inherently misleading about many of the dark patterns described in the literature, such as Conti and Sobiesk’s [9] Coercion and Forced Work patterns, Gray et al.’s [21] Forced Action dark pattern, or Mathur et al.’s [35] Obstruction pattern.”
• Nine Potential Pitfalls when Designing Human-AI Co-Creative Systems (2021, methodology): “Our research approach is related to work on dark patterns in UI/UX design [15], which also examines”