Generative AI can convert uncertainty into hypersuasive, authoritative-seeming verdicts, displacing the justificatory work on which democratic epistemic agency depends. As a corrective, I propose a Brouwer-inspired assertibility constraint for responsible AI: in high-stakes domains, systems may assert or deny claims only if they can provide a publicly inspectable and contestable certificate of entitlement; otherwise they must return Undetermined. This constraint yields a three-status interface semantics (Asserted, Denied, Undetermined) in which statuses mark entitlement to categorical speech rather than truth values of the underlying world-claim. The semantics cleanly separates internal entitlement from public standing while connecting them via the certificate as a boundary object. It also produces a time-indexed entitlement profile that is stable under numerical refinement yet revisable as the public record changes. I operationalize the constraint through decision-layer gating of threshold and argmax outputs, using internal witnesses (e.g., sound bounds or separation margins) and an output contract with reason-coded abstentions. A design lemma shows that any total, certificate-sound binary interface already decides the deployed predicate on its declared scope, so Undetermined is not a tunable reject option but a mandatory status whenever no adequate forcing witness is available. By making outputs answerable to challengeable warrants rather than confidence alone, the paper aims to preserve epistemic agency against the hypersuasive pull of automated speech in public justification.

翻译：生成式AI能将不确定性转化为具有强说服力、权威性外观的论断，挤占了民主认知自主权所依赖的证成性工作。作为纠偏方案，我提出一种受布劳威尔启发的负责任AI可断言性约束：在高风险场景下，系统只有当能够提供可公开核查与质疑的权利凭证时，才能肯定或否定某个主张；否则必须返回“未定”状态。该约束催生了一种三态接口语义（肯定、否定、未定），其中状态标记的是对范畴性言辞的权利资格，而非底层世界主张的真值。该语义清晰区分了内部权利资格与公共立场，并通过作为边界对象的凭证将二者联结；同时生成一个时间索引化的权利资格剖面——该剖面在数值精化下保持稳定，但能随公共记录的变更而修正。我通过阈值与argmax输出的决策层门控机制实现该约束，借助内部证物（如声音边界或分隔裕度）及带有理由编码的弃权输出合约。设计引理表明：任何完整的、凭证可靠的双值接口，在其声明的范围内已能判定被部署的谓词，因此“未定”并非可调节的拒绝选项，而是在缺乏足够强制证物时必须强制使用的状态。通过使输出需对可质疑的凭证负责而非仅依赖置信度，本文旨在在公共证成中对抗自动化言辞的强说服力，以维护认知自主权。