Fairness encompasses design patterns that treat users ethically and respectfully by minimizing data collection, providing clear consent mechanisms, transparent pricing, and easy exit options. This ensures users are not manipulated or deceived, and their choices are respected throughout their interaction with digital services.

Only collects and processes the minimum amount of user data necessary for the stated purpose, with explicit user permission.

Language is so clear that users easily find what they need, understand it upon first reading, and understand the consequence of their choices. In addition, language is designed for accessibility and usability, e.g. small portions of text at the right time of the user journey. When certain legalistic terms are mandatory, an information bubble in plain language is provided.

This pattern involves obtaining clear and unambiguous consent from the user before collecting, using, or sharing their personal data. This can involve providing clear explanations of how the data will be used and providing easy-to-use options for opting in or out.

Do not display false low-stock or urgency notifications. Let users browse and select products at their own pace without manipulation or stress from artificial time-limited messages. This maintains honesty and does not exploit consumers’ decision-making biases.

Design options in for example forms and pop-ups with simple, clear language so users know exactly what each action will do. This eliminates confusing or misleading options and supports informed decision making.

Processes should be as simple and short as technically possible for the user. Design userflows in a way that a user can still fully graps and if needed decide on an action, but keep unneeded additional steps away that could possibly confuse or manipulate the user.

Language is so clear that users easily find what they need, understand it upon first reading, and understand the consequence of their choices. In addition, language is designed for accessibility and usability, e.g. small portions of text at the right time of the user journey. When certain legalistic terms are mandatory, an information bubble in plain language is provided.

Emphazise (Bright Visual Interference) makes the user-friendly option more visually prominent through color, contrast, or button style. This de-emphasizes less desirable or more harmful options. For example, in the checkout process, the 'standard' (free, user-beneficial) delivery option was highlighted instead of a premium paid option, nudging users toward a choice that serves their best interest. This mirrored the way dark pattern visual interference would highlight a user-hostile option, to prioritize user well-being.

This pattern involves setting default options that are in the best interest of the user, rather than the business. For example, a default option to unsubscribe from marketing emails may be provided, rather than requiring users to opt-out.

This pattern involves friction in interaction by adding extra steps or barriers to prevent users from engaging in harmful or addictive behaviors. For example, a social media app may require users to confirm their intention before posting a potentially toxic comment, or an app intentionally take longer to open up.

Right Hierarchy describes structurally and visually ordering options so that the safest, most user-friendly, or most cost-effective choice is listed in the most prominent position (typically the top) in a menu, list, or set of radio buttons. By contrast, dark patterns often elevate user-hostile options.

This pattern involves exposing users to different perspectives or opinions that challenge their existing beliefs or preferences. For example, a news aggregator may show users articles from diverse sources.

Often implemented in external well-being and child control apps, these are patterns within apps that limit usage time to healthy levels.

The platform suggests higher quality content that is still of interest to the user, but may discourage mindless usage. This pattern respects the users well-being and attention, and does not try to exploit their curiosity or boredom into consuming more content than they need or want.

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.

This pattern involves showing users the traces or records of their data that are stored, shared, or accessed by an app or website. For example, a messaging app may show users when their messages are read, forwarded, or deleted by others.

This pattern involves showing users the detailed breakdown of the costs involved in producing and selling a product or service. This pattern respects the users curiosity and trust, and does not try to hide or inflate the margins or profits of the vendor.

The app or website transparently informs users of the categories or groups they are being placed into based on their data, allowing them to better understand how they are being profiled and how their experience may differ from other users.

The nutrition label is a design element that provides users with a clear and transparent overview about the content or impact of a particular action, decision, or data. These are becoming common in AI dataset documentation.

This pattern involves providing users with clear and understandable explanations of how their data is processed or used by an AI system. These explanations can be visual, personalized, and even counterfactual.

This pattern involves revealing the logic or criteria behind the recommendations or suggestions that are provided to the user. For example, a streaming service may explain why a certain show or movie is recommended based on the users preferences, ratings, or viewing history.