Articulating model constraints

AI systems often produce results with varying levels of certainty. While data scientists work to improve accuracy, designers must find ways to communicate these limitations to users. When designing AI-powered features, consider how to show confidence levels in predictions or recommendations. For example, a weather app might show a percentage chance of rain, while a music streaming service might place highly-recommended songs at the top of a playlist with special highlighting. Users need to understand when the system is certain versus when it's making an educated guess. Good designs include clear ways for users to provide feedback when predictions are wrong, helping improve the system while maintaining trust. Remember that hiding uncertainty often backfires when inevitable mistakes occur. Instead, transparent designs that acknowledge limitations tend to build stronger user confidence in the long run.