This position paper argues that responsible AI cannot be achieved via post-hoc governance, model cards, or behavioral alignment alone. It must also be de- signed around measurable constraints on efficiency, auditability, robustness, and human oversight. We propose that neuroscience offers a principled source of such constraints through two complementary lenses. The instrumental lens imports brain-derived design priors - sparsity, event-driven computation, local learning, modular routing, dendritic computation, and neuromorphic substrates - to improve efficiency and governability. The epistemic lens adapts neuroscience tools for studying opaque biological systems, including representational geometry, topol- ogy, and cognitive stress tests, to make AI systems more legible, contestable, and accountable without requiring complete mechanistic decomposition. We opera- tionalize this agenda through a minimal benchmark suite that pairs energy and CO2e audits with representation audits, robustness probes, and cognitive bias tests, and through deployment patterns for abstention, deferral, oversight logging, and event-driven edge computation. Rather than treating the brain as a template to copy, we argue that neuroscience should serve as a source of falsifiable design hypotheses for building AI systems that are capable, sustainable, and accountable by design.