Deceptive patterns trick people into doing things they didn’t mean to.
Also known as “dark patterns,” they’re features of apps, websites and AI systems that stop you doing what you want, or steer you into harmful decisions you would not have made deliberately.
Addictive Design The user interacts with the product excessively, because its design exploits psychological vulnerabilities to foster compulsive behaviour. Comparison prevention The user struggles to compare products because features and prices are combined in a complex manner, or because essential information is hard to find. Confirmshaming The user is emotionally manipulated into doing something that they would not otherwise have done. Currency Confusion The user is misled about how much they are really spending, because real money is converted into a virtual currency that obscures the true cost. Disguised ads The user mistakenly believes they are clicking on an interface element or native content, but it's actually a disguised advertisment. Fake scarcity The user is pressured into completing an action because they are presented with a fake indication of limited supply or popularity. Fake social proof The user is misled into believing a product is more popular or credible than it really is, because they were shown fake reviews, testimonials, or activity messages. Fake urgency The user is pressured into completing an action because they are presented with a fake time limitation. Forced action The user wants to do something, but they are required to do something else undesirable in return. Hard to cancel The user finds it easy to sign up or subscribe, but when they want to cancel they find it very hard. Hidden costs The user is enticed with a low advertised price. After investing time and effort, they discover unexpected fees and charges when they reach the checkout. Hidden subscription The user is unknowingly enrolled in a recurring subscription or payment plan without clear disclosure or their explicit consent. Nagging The user tries to do something, but they are persistently interrupted by requests to do something else that may not be in their best interests. Obstruction The user is faced with barriers or hurdles, making it hard for them to complete their task or access information. Preselection The user is presented with a default option that has already been selected for them, in order to influence their decision-making. Sneaking The user is drawn into a transaction on false pretences, because pertinent information is hidden or delayed from being presented to them. Trick wording The user is misled into taking an action, due to the presentation of confusing or misleading language. Visual interference The user expects to see information presented in a clear and predictable way on the page, but it is hidden, obscured or disguised.
Amazon 31 examples Google 22 examples Microsoft 21 examples Facebook 20 examples Twitter 15 examples Adobe 13 examples HP 11 examples Linkedin 10 examples Instagram 9 examples Trump 9 examples Apple 7 examples Meta 7 examples Yahoo 7 examples New York Times 6 examples Booking.com 5 examples Doordash 5 examples Figma 5 examples 5 examples
GDPR (EU) 70 enforcements Spanish Law on Information Society Services 22 enforcements FTC Act (US) 15 enforcements Restore Online Shoppers’ Confidence Act (US) 7 enforcements Consumer Protection Laws (UK) 5 enforcements ePrivacy (EU) 5 enforcements District of Columbia Consumer Protection Procedures Act 3 enforcements Italian Consumer Law (EU) 3 enforcements PECR (UK) 3 enforcements Electronic Fund Transfer Act (US) 2 enforcements Unfair Competition Law (US) 2 enforcements Austria Data Protection Law (EU) 1 enforcement
EU Meta Platforms Ireland Limited, and Instagram Social Media Network's Investigation by DPC USA In the Matter of Epic Games, Inc EU Ireland's Data Protection Commission Investigation into WhatsApp Ireland Limited USA FTC v. Prog Leasing EU Deliberation of the Restricted Committee concerning Google LLC and Google Ireland Limited USA Federal Trade Commission v. Vonage Holdings Corporation USA State of Arizona, ex rel. Mark Brnovich, Attorney General v. Google, LLC EU Deliberation of the Restricted Committee concerning Microsoft Ireland Operations Limited EU Deliberation of the Restricted Committee concerning Facebook Ireland Limited USA Geraldine Mahood v. Noom, Inc. EU NGO la Quadrature du Net (LQDN) and Noyb (Complainant) v. Google LLC USA United States of America v. MyLife.com, Inc., and Jeffrey Tinsley
Academic Scholar Consent or Coercion? Dark Patterns and the Illusion of User Autonomy under India’s Digital Personal Data Protection Framework Academic Scholar Privacy and Manipulation in the Platform Economy: An EU Framework for Regulating Dark Patterns Academic Scholar Cognitive Traits Related to Choice Behavior in User Interfaces with Visually Interfering Dark Patterns Academic Scholar Designing Against the User: Dark Patterns as the Inversion of Human-Centered Design Academic Scholar Ethical imperatives of digital design in journalism and advertising: a media-critical approach to visual literacy Academic Scholar Beyond Manipulation: How Users Perceive Harmful AI Chatbot Interactions Academic Scholar The Intersection of the Digital Personal Data Protection Act, 2023, and Consumer Rights Academic Scholar Deep Learning-Based Detection of Deceptive UI Patterns in Web Applications Using Convolutional Neural Networks Academic Scholar DPAF: A lifecycle-integrated framework for dark pattern mitigation with quantitative audit metrics Academic Scholar The Subscription Tomb: Legal and Ethical Challenges of Auto-renewal Traps in the UAE Academic Scholar How Dark Patterns Shape Consumer Behavior on Social Media: A Systematic Literature Review and Research Agenda Academic Scholar Ethics of Digital Marketing in the AI Era: A Structured Thematic Review of Recent Research, 2023–2025