How UX Can (and Should) Humanize Machine Learning
Google’s Michelle Carney talks on ways data science and UX can better collaborate.
Increasingly, AI and ML are shaping the way we build products and design experiences.
And increasingly, user-centric organizations will need to inject qualitative data into the creation of models, algorithms, and interactions.
In this webinar, Google’s Michelle Carney walks us through the “whys” and “hows” of making UX-driven machine learning more human.
Get the recording!
You'll learn...
- How user research can (and should) guide the fast-growing machine learning landscape.
- Tactics for better enabling collaboration between UXR and data science at your organization.
- Guidelines for collecting early-stage user data for ML models, featuring an example dscout use case.
Meet the speakers
Michelle Carney
UX Researcher, Machine Learning + AI Google
Michelle has worked at the intersection of UX and ML for Amazon and Google, and has lectured on the topic at Stanford University's d.school. She's the founder of the MLUX Meetup and has worked as a Computational Neuroscientist, UX researcher and UI engineer.
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Ben Wiedmaier
Product Evangelist,
dscout
Ben is the product evangelist at dscout, where he spreads the “good news” of contextual research, helps customers understand how to get the most from dscout. He has a doctorate in communication studies, studying “nonverbal courtship signals”, a.k.a. flirting. No, he doesn’t have dating advice for you.