![]() ML models will reflect the data they are trained on, so analyze your raw data carefully to ensure you understand it. This will build a rich variety of user perspectives into the project and increase the number of people who benefit from the technology. Engage with a diverse set of users and use-case scenarios, and incorporate feedback before and throughout project development.Technically, it is much more difficult to achieve good precision at one answer versus precision at a few answers (e.g., Model potential adverse feedback early in the design process, followed by specific live testing and iteration for a small fraction of traffic before full deployment. In other cases, it may be optimal for your system to suggest a few options to the user. Consider augmentation and assistance: producing a single answer can be appropriate where there is a high probability that the answer satisfies a diversity of users and use cases.Design features with appropriate disclosures built-in: clarity and control is crucial to a good user experience.The way actual users experience your system is essential to assessing the true impact of its predictions, recommendations, and decisions.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |