Explainability
Explainability encompasses design patterns that make system operations, data handling, and algorithmic decisions transparent and understandable to users. This includes showing data traces, revealing recommendation logic, providing clear cost breakdowns, and explaining AI processes in accessible language. These patterns build trust and empower users to make informed decisions about their interactions with digital systems.
Interaction Contexts
- checkout
- settings
- social media
Supported Goals
- clarity
- transparency
- satisfaction
Symbiosis
Dark counterparts to this bright pattern
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Hiding InformationView pair →Withholds or delays key details, making it harder for users to make fully informed decisions during their interaction. -
Hidden InformationView pair →Relevant details or options are concealed or presented as unimportant, making it harder for users to access or recognize them. -
Interface InterferenceView pair →Make certain actions easier to find or perform while confusing or hiding alternatives.
Sources
Pattern Levels
Source not found.
Approach: semantic vs flipping
Two different approaches to Bright Patterns:
1
Semantic Approach
This approach is used by Sandhaus. It defines concrete Bright Patterns for specific contexts — for example the Bright Pattern "Usage Limits", which describes an interface that restricts the usage time of a service to a healthy level.
2
Flipping Dark Patterns
The original way the term "Bright Pattern" was introduced: the direction of the manipulation is switched from harming the user to being user-friendly. For example, instead of highlighting the option that harms the user, the user-friendly option is highlighted.
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