The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Shining a Light on Dark Patterns
Open accessAbstract
Dark patterns are user interfaces whose designers knowingly confuse users, make it difficult for users to express their actual preferences, or manipulate users into taking certain actions. They typically exploit cognitive biases and prompt online consumers to purchase goods and services that they do not want or to reveal personal information they would prefer not to disclose. This article provides the first public evidence of the power of dark patterns. It discusses the results of the authors’ two large-scale experiments in which representative samples of American consumers were exposed to dark patterns. In the first study, users exposed to mild dark patterns were more than twice as likely to sign up for a dubious service as those assigned to the control group, and users in the aggressive dark pattern condition were almost four times as likely to subscribe. Moreover, whereas aggressive dark patterns generated a powerful backlash among consumers, mild dark patterns did not. Less educated subjects were significantly more susceptible to mild dark patterns than their well-educated counterparts. The second study identified the dark patterns that seem most likely to nudge consumers into making decisions that they are likely to regret or misunderstand. Hidden information, trick question, and obstruction strategies were particularly likely to manipulate consumers successfully. Other strategies employing loaded language or generating bandwagon effects worked moderately well, while still others such as “must act now” messages did not make consumers more likely to purchase a costly service. Our second study also replicated a striking result in the first experiment, which is that where dark patterns were employed the cost of the service offered to consumers became immaterial. Decision architecture, not price, drove consumer purchasing decisions. The article concludes by examining legal frameworks for addressing dark patterns. Many dark patterns appear to violate federal and state laws restricting the use of unfair and deceptive practices in trade. Moreover, in those instances where consumers enter into contracts after being exposed to dark patterns, their consent could be deemed voidable under contract law principles. The article also proposes that dark pattern audits become part of the Federal Trade Commission (FTC)’s consent decree process. Dark patterns are presumably proliferating because firms’ proprietary A-B testing has revealed them to be profit maximizing. We show how similar A-B testing can be used to identify those dark patterns that are so manipulative that they ought to be deemed unlawful.
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Cited by
The AI-mediated intimacy economy: a paradigm shift in digital interactions
Bozdağ, A. A. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02132-6 · Cited by 42
Impact of Voice Fidelity on Decision Making: A Potential Dark Pattern?
Dubiel, M., Sergeeva, A., & Leiva, L. A. (2024) · Proceedings of the 29th International Conference on Intelligent User Interfaces · doi:10.1145/3640543.3645202 · Cited by 16
Krauß, V., McGill, M., Kosch, T., Thiel, Y. M., Schön, D., & Gugenheimer, J. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713083 · Cited by 13
How is "Public Policy" Used in HCI Scholarship?
Gairola, R., & Gray, C. M. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3719997 · Cited by 8
Busch, C. (2025) · German Law Journal · doi:10.1017/glj.2026.10188 · Cited by 5
Metadata
- Authors
- Luguri, J., & Strahilevitz, L. J.
- Venue
- Journal of Legal Analysis
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-01-01
- Volume
- 13
- Issue
- 1
- Pages
- 43-109
- ISSN
- 1946-5319 (ISSN-L)
- 2161-7201
- OpenAlex
- W3137759237
- Cited by
- 465
- References
- 3
- FWCI
- 14.35
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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