Human-Centered Machine Learning
As was the case with the mobile revolution, and the web before that, ML will cause us to rethink, restructure, displace, and consider new possibilities for virtually every experience we build. In the Google UX community, we’ve started an effort called “human-centered machine learning” (HCML) to help focus and guide that conversation. Using this lens, we look across products to see how ML can stay grounded in human needs while solving them in unique ways only possible through ML. Our team at Google works with UXers across the company to bring them up to speed on core ML concepts, understand how to integrate ML into the UX utility belt, and ensure ML and AI are built in inclusive ways. We’ve developed seven points to help designers navigate the new terrain of designing ML-driven products. Born out of our work with UX and AI teams at Google (and a healthy dose of trial and error), these points will help you put the user first, iterate quickly, and understand the unique opportunities ML creates.
- Don’t expect Machine learning to figure out what problems to solve
- Ask yourself if ML will address the problem in a unique way
- Fake it with personal examples and wizards
- Weigh the costs of false positives and false negatives
- Plan for co-learning and adaptation
- Teach your algorithm using the right labels
- Extend your UX family, ML is a creative process
via https://medium.com/google-design/human-centered-machine-learning-a770d10562cd