👩🏻‍🔬How to do User Research for AI Products with Lauren M. Kaplan, PhD AI Studios with Natalia Burina

What is User Research and why is it useful for AI product development? Lauren Kaplan is a mixed methods researcher passionate about inclusion, leveraging technology for social good, and learning. At Meta, she led research on Privacy Preserving Machine Learning (PPML) and PyTorch (an Open Source AI framework) advocating for people centric AI.
⏰ 0:49 About Lauren
⏰ 1:11 What is Mixed Methods UXR
⏰ 1:27 What is User Research
⏰ 2:10 How to match User Research with Product Development
⏰ 3:13 What are the benefits of User Research for AI Products
⏰ 4:00 What's the difference between User Research and User Feedback
⏰ 6:45 Challenges of doing User Research for AI
⏰ 9:05 How to approach User Research for Generative AI
⏰ 10:20 Privacy Preserving ML User Research
⏰ 12:23 Synthetic Users
⏰ 16:05 How to get into AI User Research
⏰ 17:22 How Lauren stays on top of AI News and Advancements
⏰ 19:10 How to do User Research for Open Source AI
⏰ 21:47 Working with AI Researchers and bridging the discipline gap
⏰ 23:06 How should AI Researchers ensure they're people centric
⏰ 26:45 What stood out about AI Privacy vs other AI
⏰ 29:15 What was it like to work on PyTorch
⏰ 31:30 What AI Lauren is excited about next
Referenced
* Mapping Strategic, Iterative, and Evaluative Research to Product: Matt's UXR Process & FAQ
* Google PAIR resources: People + AI Research - Chapters
* “How can companies help people understand privacy-enhancing technologies like on-device learning?” 
* The Future of AI is People-Centered
* Mapping qualitative and quantitative methods Comparing UX Research Methods
* Synthetic Users: [2209.06899] Out of One, Many: Using Language Models to Simulate Human Samples
Where to find Lauren & her work
* LinkedIn
* Twitter
Where to find Natalia
* Twitter
* LinkedIn


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