Dark pattern: Difference between revisions

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==Common types and examples==

Research has identified numerous specific dark patterns, with one comprehensive study proposing a taxonomy comprising 68 distinct types. These manifest across various industries and digital contexts.

Research has identified numerous specific dark patterns, with one comprehensive study proposing a taxonomy comprising 68 distinct types. These manifest across various industries and digital contexts.<ref name=":3" />

===Obstruction patterns===

These designs make desired actions (like rejecting tracking) significantly more difficult than accepting alternatives. A classic example is the ''Roach Motel'' pattern, where signing up for a service is straightforward but cancellation is excessively difficult. The FTC highlighted this pattern in their case against ABCmouse, where cancellation was made "extremely difficult" despite promising "Easy Cancellation."

These designs make desired actions (like rejecting tracking) significantly more difficult than accepting alternatives. A classic example is the ''Roach Motel'' pattern, where signing up for a service is straightforward but cancellation is excessively difficult. The FTC highlighted this pattern in their case against ABCmouse, where cancellation was made "extremely difficult" despite promising "Easy Cancellation."<ref>{{Cite web |author=Keller and Heckman LLP |date=2020-09-28 |title=FTC Targets Negative Option Schemes in Two Multimillion Dollar Settlements |url=https://www.lexology.com/library/detail.aspx?g=a2def591-a71f-477d-8f39-55f9b40ec125 |access-date=2025-11-08 |website=Lexology}}</ref>

===Interface interference===

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==Detection, avoidance and mitigation==

===Technical detection and tools===

Efforts to automatically detect dark patterns are evolving but face significant challenges. A comprehensive study found that existing tools could only identify 31 of 68 identified dark pattern types, a coverage rate of just 45.5%.<ref>{{Cite web |last=Li |first=Meng |last2=Wang |first2=Xiang |last3=Nei |first3=Liming |last4=Li |first4=Chenglin |last5=Liu |first5=Yang |last6=Zhao |first6=Yangyang |last7=Xue |first7=Lei |last8=Kabir Sulaiman |first8=Said |date=2024-12-12 |title=[2412.09147] A Comprehensive Study on Dark Patterns |url=https://arxiv.org/abs/2412.09147 |access-date=2025-11-08 |website=arXiv |doi=10.48550/arXiv.2412.09147}}</ref> The study proposed a Dark Pattern Analysis Framework (DPAF) to address existing gaps.

Efforts to automatically detect dark patterns are evolving but face significant challenges. A comprehensive study found that existing tools could only identify 31 of 68 identified dark pattern types, a coverage rate of just 45.5%.<ref name=":3">{{Cite web |last=Li |first=Meng |last2=Wang |first2=Xiang |last3=Nei |first3=Liming |last4=Li |first4=Chenglin |last5=Liu |first5=Yang |last6=Zhao |first6=Yangyang |last7=Xue |first7=Lei |last8=Kabir Sulaiman |first8=Said |date=2024-12-12 |title=[2412.09147] A Comprehensive Study on Dark Patterns |url=https://arxiv.org/abs/2412.09147 |access-date=2025-11-08 |website=arXiv |doi=10.48550/arXiv.2412.09147}}</ref> The study proposed a Dark Pattern Analysis Framework (DPAF) to address existing gaps.

===Ethical design alternatives===

@@ Line 13: / Line 13: @@
 ==Common types and examples==
-Research has identified numerous specific dark patterns, with one comprehensive study proposing a taxonomy comprising 68 distinct types. These manifest across various industries and digital contexts.
+Research has identified numerous specific dark patterns, with one comprehensive study proposing a taxonomy comprising 68 distinct types. These manifest across various industries and digital contexts.<ref name=":3" />
 ===Obstruction patterns===
-These designs make desired actions (like rejecting tracking) significantly more difficult than accepting alternatives. A classic example is the ''Roach Motel'' pattern, where signing up for a service is straightforward but cancellation is excessively difficult. The FTC highlighted this pattern in their case against ABCmouse, where cancellation was made "extremely difficult" despite promising "Easy Cancellation."
+These designs make desired actions (like rejecting tracking) significantly more difficult than accepting alternatives. A classic example is the ''Roach Motel'' pattern, where signing up for a service is straightforward but cancellation is excessively difficult. The FTC highlighted this pattern in their case against ABCmouse, where cancellation was made "extremely difficult" despite promising "Easy Cancellation."<ref>{{Cite web |author=Keller and Heckman LLP |date=2020-09-28 |title=FTC Targets Negative Option Schemes in Two Multimillion Dollar Settlements |url=https://www.lexology.com/library/detail.aspx?g=a2def591-a71f-477d-8f39-55f9b40ec125 |access-date=2025-11-08 |website=Lexology}}</ref>
 ===Interface interference===
@@ Line 69: / Line 69: @@
 ==Detection, avoidance and mitigation==
 ===Technical detection and tools===
-Efforts to automatically detect dark patterns are evolving but face significant challenges. A comprehensive study found that existing tools could only identify 31 of 68 identified dark pattern types, a coverage rate of just 45.5%.<ref>{{Cite web |last=Li |first=Meng |last2=Wang |first2=Xiang |last3=Nei |first3=Liming |last4=Li |first4=Chenglin |last5=Liu |first5=Yang |last6=Zhao |first6=Yangyang |last7=Xue |first7=Lei |last8=Kabir Sulaiman |first8=Said |date=2024-12-12 |title=[2412.09147] A Comprehensive Study on Dark Patterns |url=https://arxiv.org/abs/2412.09147 |access-date=2025-11-08 |website=arXiv |doi=10.48550/arXiv.2412.09147}}</ref> The study proposed a Dark Pattern Analysis Framework (DPAF) to address existing gaps.
+Efforts to automatically detect dark patterns are evolving but face significant challenges. A comprehensive study found that existing tools could only identify 31 of 68 identified dark pattern types, a coverage rate of just 45.5%.<ref name=":3">{{Cite web |last=Li |first=Meng |last2=Wang |first2=Xiang |last3=Nei |first3=Liming |last4=Li |first4=Chenglin |last5=Liu |first5=Yang |last6=Zhao |first6=Yangyang |last7=Xue |first7=Lei |last8=Kabir Sulaiman |first8=Said |date=2024-12-12 |title=[2412.09147] A Comprehensive Study on Dark Patterns |url=https://arxiv.org/abs/2412.09147 |access-date=2025-11-08 |website=arXiv |doi=10.48550/arXiv.2412.09147}}</ref> The study proposed a Dark Pattern Analysis Framework (DPAF) to address existing gaps.
 ===Ethical design alternatives===