For Questions of Ought, AI Could Use Some SAGE Advice

AI models are increasingly used for normative assistance, guidance on what people ought to do or think. However, in open-ended contexts, models have a tendency to collapse to only a homogeneous set of considerations, inadvertently creating epistemic bubbles where other relevant voices and perspectives are left out. We propose simulation-augmented generation (SAGE) as an alternative paradigm that directly brings society-in-the-loop. At inference time, a model selects an appropriate reference population for the prompt at hand (optionally adjustable by the user), queries generative simulations of individuals from that population for their open-ended perspectives, and uses these inputs to return a final response for the user. By helping users access the landscape of societal perspectives—rather than serving as an opaque arbiter of judgment—SAGE assists users in normative choices while upholding their agency.