Personalized defaults
Also known as: Smart defaults
Pre-set options tailored to each individual's likely preferences, rather than one default for everyone.
What it means
Personalized defaults use data about a person — past behavior, stated preferences, or attributes — to set the no-action option to what that individual would most plausibly want, instead of applying a single mass default to a heterogeneous population. The motivation is that defaults are extraordinarily powerful but a one-size-fits-all setting necessarily misfits many people; tailoring the default promises to capture the benefit of inertia while better matching true preferences. They sit between mass defaults and active choosing, and can be highly effective, but they raise sharper ethical and practical concerns: they require personal data, can entrench profiling errors, and may manipulate if the predicted 'preference' actually serves the architect. Transparency and the right to override are key safeguards. They matter because as data and personalization grow, the default — already the strongest nudge — becomes individually targeted, magnifying both its potential to help and its potential to exploit.
Examples
A retirement platform sets each new hire's default contribution and fund mix based on their age and salary, rather than enrolling everyone at the same generic rate.
A smart thermostat sets each home's default schedule from its own occupancy pattern rather than shipping everyone the same 7am-to-9pm setting — most people never change it, so the guess matters.
A streaming service defaults you to autoplay because your history says you would want it — a prediction that also happens to serve the platform's watch time, which is exactly why overriding must stay easy.
First described in Smith, Goldstein & Johnson (2013).