Bright patterns are benevolent design solutions that prioritize user's goals and well-being over their desires and business objectives. These ethical user interfaces may break with established design conventions of 'good' design. For more information read our CHI '23 Workshop paper Promoting Bright Patterns.
While bright patterns are not commonly found, we have discovered a few instances of these rare designs in the wild. If you have encountered any bright pattern that is not covered by the examples below, please submit it using this form: Submit a Bright Pattern.
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 takes longer to open up.
Making it easy for users to leave a situation or cancel a subscription. For example, providing a clear and accessible option to cancel membership or delete profile.
This pattern involves obtaining clear and unambiguous consent from the user before collecting, using, or sharing their personal data. This can involve providing clear visual explanation of how the data will be used and providing easy-to-use options for opting in or out.
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.
The nutrition label is a design element that provides users with clear and transparent overview about the content or impact of a particular action or decision. These are becoming common in AI dataset documentation.
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 platform suggests higher quality content, still of interest to the user, but may discourage mindless usage. This pattern respects the user’s well-being and attention, and does not try to exploit their curiosity or boredom into consuming more content than they need or want. For example, a social media app may suggest users to watch educational videos or read informative articles instead of scrolling through endless feeds of memes or gossip, or a gaming app may suggest users to play games that are more challenging or rewarding instead of games that are more addictive or repetitive.
Often implemented in external well-being and child control apps, these are patterns within apps that limit usage time to healthy levels.
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 user’s preferences, ratings, or viewing history, or an e-commerce site may disclose how sponsored products are ranked or selected.
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. For example, a health app may visually show how it calculates the user's risk of developing a certain condition based on their data, or a credit-scoring system may provide a personalized explanation of how it determines the user's creditworthiness based on their data.
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, or a search engine may show users their search history and how it affects their results.
This pattern involves showing users the detailed breakdown of the costs involved in producing and selling a product or service. This pattern respects the user’s curiosity and trust, and does not try to hide or inflate the margins or profits of the vendor. For example, showing users how much the product cost in purchase, marketing, return and shipping.
This pattern involves exposing users to different perspectives or opinions frequently that challenge their existing beliefs or preferences. For example, a news aggregator may show users articles from diverse sources or a music streaming service may suggest songs from genres that they usually don’t listen to.
This pattern provides critique on consumed and posted content. For example, it shows, views with different political leanings than theirs, and makes users reflect on their opinions.