From Prediction to Accountable Action: Designing Uncertainty in Femtech AI
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… [...]
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