Full-stack designer— discovery, user research, design, testing
A project manager
2 data scientists
Summer 2019, 4 Weeks
Success meant a growing number of data labeling, which would increase the accuracy of the AI prediction. Our team’s primary goal was to simplify the data tagging feature so users could easily and quickly train AI to learn social media’s content. In addition to that were higher-level goals for the business: increase the trustworthiness of the software, transform tagging labeling process into a streamlined experience, enable users to correct the AI easily, and help users to understand the AI model they have built.
We drew inspirations from systems that have similar categorization functions such as bookmarks on social media posts, tagging on blog posts, and file labeling in the computer systems. Hence, this could simplify the learning process for users.
Moreover, early users interviews show that users want to know the source and creator of the posts, and the number of reactions to the post in order to grasp the virality of the topic.
How to adapt to changing requirements
New timelines, resourcing issues, and reprioritization meant the scope of the project was constantly changing. I had to adapt to those changes and still deliver the best design in time with tight deadlines.
Always fight for good UX
I had to work under very strict technical constraints of machine learning, but still fight for what I believe is essential to having a good user experience.