From Prediction to Accountable Action: Designing Uncertainty in Femtech AI

본문 미리보기

In women’s health, accuracy is necessary, but it is only the first layer of product safety. It’s important for the system to know when a prediction is strong enough to act on and is built to say so when it isn’t. Open most fertility or cycle-tracking apps, and you’ll see a clean result: ovulation on day 15, a high-fertility label, a confidence score of 78%. The number looks precise. The biology underneath it is not. A period date is something the user observed. Ovulation is a latent event no…