Michelle Carney on Building More Human, Helpful, and Ethical Systems
Alum Michelle Carney talks us through tactics for building more human, more helpful, and more ethical AI + ML systems.
From the article:
Can you do UX Research on machine learning systems? Can you get feedback from real users before the AI has been built? Can you even test an ML system before you have a production-ready model?
As a researcher in the increasingly crowded venn diagram between UX and ML—these are the questions I get often.
And my answers are: yes, please, and you should!
The combination of ML and UX can create really powerful products, like Visual Discovery by Pinterest, or Google’s Smart Compose. And interest in the intersection is growing (our Machine Learning and User Experience Meetup has grown up to 2000+ members strong).
This case study outlines my best practices for doing research on ML models before they’re production-ready built. It involves using data science/ML techniques (like unsupervised learning) to make data-driven design decisions. And it harnesses UX tactics to help make AI/ML systems more explainable and transparent to the end user—helping them to understand the ways they can better control their experience.
To read the full article, please visit here!