DBTB INT Hunter Whitney r
summary
Argues that user experience and machine learning enter a tight, unpredictable coevolution of humans and machines where interfaces both observe and adapt, not just display. Describes teaching a class with data scientists to bridge gaps, stresses practical work on data‑intensive visualizations and the need to consider human fingerprints on data. Claims UX can shape outcomes by negotiating tradeoffs between automated models and human judgment and calls for concrete collaboration practices between designers and data teams.
AI-written summary · about
related talks
31mWhy data science and UX research should be best friends
23mUser Experience Now Includes Machine Experience
06mData + Empathy: How to Approach UX Decisions in Web3
50mDVUX: Data Visualization and User Experience
08mChris Hass - UXPA 2017
43mUX + Data = Love
27mContinous integration of ML products and UX design
02mInteract London 2019: The Human & The Machine