… Google. We hope they are useful to you as you think through your own ML-powered product questions. As ML starts to power more and more products and experiences, let’s step up to our responsibility to stay human-centered, find the unique value for people, and make every experience great.
So inspire them with examples — decks, personal stories, vision videos, prototypes, clips from user research, the works — of what an amazing experience could look and feel like, build up their fluency in user research goals and findings, and gently introduce them to our wonderful world of UX crits, workshops, and design sprints to help manifest a deeper understanding of your product principles and experience goals. The earlier they get comfortable with iteration, the better it will be for the robustness of your ML pipeline, and for your ability to effectively influence the product.
…ning is a much more creative and expressive engineering process than we’re generally accustomed to. Training a model can be slow-going, and the tools for visualization aren’t great yet, so engineers end up needing to use their imaginations frequently when tuning an algorithm (there’s even a methodology called “Active Learning” where they manually “tune” the model after every iteration). Your job is to help them make great user-centered choices all along the way.