Lead
Imagine an app displays: "Distress cry 70%, hunger cry 20%, other 10%."
One parent reads this and thinks: "70% — that's probably distress, then." Another reads it and thinks: "70% means it's wrong 30% of the time. That's not reliable." Neither reading is quite right.
Probabilistic estimates from AI-powered features are showing up in more parenting apps and consumer products. The ability to read those estimates clearly — to understand what the number is actually claiming, and what it isn't — is a form of literacy that matters more as AI becomes more embedded in daily tools. This article is about what "70%" means in the context of a machine learning model, what calibration is, and how to use probabilistic output without being misled by it.