So our first point is that you still need to do all that hard work you’ve always done to find human needs. This is all the ethnography, contextual inquiries, interviews, deep hanging out, surveys, reading customer support tickets, logs analysis, and getting proximate to people to figure out if you’re solving a problem or addressing an unstated need people have. Machine learning won’t figure out what problems to solve. We still need to define that. As UXers, we already have the tools to guide our teams, regardless of the dominant technology paradigm.
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.
Machine learning (ML) is the science of helping computers discover patterns and relationships in data instead of being manually programmed. It’s a powerful tool for creating personalized and dynamic experiences, and it’s already driving everything from Netflix recommendations to autonomous cars. But as more and more experiences are built with ML, it’s clear that UXers still have a lot to learn about how to make users feel in control of the technology, and not the other way round.