What it means
The product learns behavior from examples (clicks, labels, documents, feedback) and updates its predictions over time or across user segments.
Why designers should care
ML-backed features need UX for cold start (no data yet), drift (behavior changes), and feedback loops so bad predictions do not get reinforced silently.
Example
An onboarding recommender ranks setup steps based on similar teams. First-time users see generic defaults; returning admins see tuned suggestions, with copy that explains why items are ranked.
Common mistakes
- Assuming the model is equally accurate for all users on day one.
- Collecting feedback with no visible effect, eroding trust in “smart” ranking.
- Using “AI” marketing language when the feature is mostly rules-based.
Frequently asked questions
What is Machine Learning?
Machine learning (ML) is a branch of AI where systems improve at a task by finding patterns in data instead of being explicitly programmed for every scenario.
Why should designers care about Machine Learning?
ML-backed features need UX for cold start (no data yet), drift (behavior changes), and feedback loops so bad predictions do not get reinforced silently.
What are common mistakes with Machine Learning?
Assuming the model is equally accurate for all users on day one; Collecting feedback with no visible effect, eroding trust in “smart” ranking; Using “AI” marketing language when the feature is mostly rules-based.