ALIGN: Auditing Language-Model Intergroup Guard Neutrality

Open guard models increasingly mediate what deployed language-model systems treat as "harmful," emitting a single safe/unsafe verdict per interaction. Yet human judgments can diverge sharply: on DICES-350, racial group majorities directly oppose each other on 77 of 350 conversations (22\%). We propose an audit protocol for evaluating open guards against disaggregated human judgments rather than a single collapsed label, and we demonstrate it on four open models. Applying the protocol reveals three patterns. First, every guard shows substantial differences in alignment across racial-group majority targets: full-set AUC gaps range from .03--.14 and widen to .14--.23 on group-conflicted items, with all full-set bootstrap CIs excluding zero. Second, guard confidence correlates with rater disagreement in guard-specific ways, from WildGuard ($\rho = -.44$) to ShieldGemma-9B ($\rho = +.12$, uncorrected $p = .02$). Third, apparent differences in which group a guard best tracks are purely descriptive (non-significant at $K=5$) and should not be interpreted as distinct cross-guard ``bias profiles.'' An agreement-based achievable-alignment analysis shows that DICES-350's disagreement structure does not itself preclude substantially more balanced binary agreement, providing a model-free reference for interpreting guard disparities. We release the audit harness and argue that guard model cards should report perspectival alignment profiles alongside aggregate performance.