What counts as a dark pattern
Dark patterns, named by Brignull (2023), are deliberate. Gray et al. (2018) grouped them as nagging, obstruction, sneaking, interface interference and forced action. Confirmshaming ('No thanks, I hate saving money') and the roach motel, easy to enter and hard to leave, are classics. They exploit judgement habits much like a nudge, but against the user, closer to 'sludge'.
How common and how effective
Mathur et al. (2019) crawled about 11,000 shopping sites and found 1,818 instances such as fake low-stock warnings and countdowns. Di Geronimo et al. (2020) found at least one in 95% of 240 popular mobile apps, mostly unnoticed by users. In Luguri and Strahilevitz's (2021) experiments, even mild dark patterns more than doubled paid sign-ups, with larger effects on less-educated people. Small screens make refusal links easy to hide.
Consent screens, regulation and alternatives
Nouwens et al. (2020) showed removing 'reject' from the first layer of cookie pop-ups sharply raises consent, and Utz et al. (2019) found banner position and defaults steer choices. Regulation is growing, from the EU Digital Services Act to FTC enforcement and Korean e-commerce rules, making dark patterns an issue for consumer protection law and data protection. Alternatives: cancellation as easy as sign-up, equal-size yes/no buttons and unticked boxes.
FAQ
- How do dark patterns differ from nudges?
- Both use judgement habits, but nudges aim to benefit users while dark patterns benefit the business at the user's expense.
- Are dark patterns illegal?
- It depends on country and type; hidden subscriptions and obstructed cancellation are increasingly regulated.
Sources reviewed: 2026-10-27
References
- Gray, C. M., Kou, Y., Battles, B., Hoggatt, J., & Toombs, A. L. (2018). The dark (patterns) side of UX design. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–14). ACM. Proposes five strategies: nagging, obstruction, sneaking, interface interference and forced action.
- Mathur, A., Acar, G., Friedman, M. J., Lucherini, E., Mayer, J., Chetty, M., & Narayanan, A. (2019). Dark patterns at scale: Findings from a crawl of 11K shopping websites. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), Article 81, 1–32. Crawled 11,000 shopping sites and found 1,818 instances, showing real prevalence.
- Luguri, J., & Strahilevitz, L. J. (2021). Shining a light on dark patterns. Journal of Legal Analysis, 13(1), 43–109. Experiments showing dark patterns more than double sign-ups, hitting less-educated users hardest; key for legal debate.
- Di Geronimo, L., Braz, L., Fregnan, E., Palomba, F., & Bacchelli, A. (2020). UI dark patterns and where to find them: A study on mobile applications and user perception. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–14). ACM. Found dark patterns in 95% of 240 popular mobile apps, mostly unnoticed by users.
- Nouwens, M., Liccardi, I., Veale, M., Karger, D., & Kagal, L. (2020). Dark patterns after the GDPR: Scraping consent pop-ups and demonstrating their influence. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM. Shows cookie-consent pop-up design strongly shifts consent rates.
- Utz, C., Degeling, M., Fahl, S., Schaub, F., & Holz, T. (2019). (Un)informed consent: Studying GDPR consent notices in the field. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 973–990). ACM. Field experiments on how banner position, defaults and choices change behaviour.
- Brignull, H. (2023). Deceptive patterns: Exposing the tricks tech companies use to control you. Testimonium. By the person who coined the term, with real examples by type.