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No Certificate, No Categorical Speech Act: A Brouwerian Assertibility Constraint for Public Reason

arXiv 2603.03971 ↗First posted 4 Mar 2026cs.CY cs.AI cs.LGcited by 0

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Abstract (arXiv)

Generative AI can convert uncertainty into 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) whose statuses mark entitlement to categorical speech rather than truth values of the underlying world-claim. The framework distinguishes internal entitlement from public standing while connecting them through the certificate as a boundary object. Philosophically, it develops a form of applied intuitionism: resistance to unwarranted completeness becomes a norm of public machine speech, under which categorical commitment requires exhibited and contestable entitlement. I operationalize the constraint through structured certificates, decision-layer gates for threshold and argmax predicates, and a reason-coded output contract. A design lemma shows that, under a refutation-soundness condition on the contractually specified negative side, certificate-sound binary totality presupposes witnessed decidability on the declared scope. Under the output contract, Undetermined is therefore not a tunable reject option but a mandatory status whenever the available certificates do not uniquely license assertion or denial. By making outputs answerable to contestable warrants rather than confidence alone, the paper aims to preserve epistemic agency where automated speech enters public justification.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 58 references that are in the map. First posted on arXiv 4 Mar 2026.

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References · 9

  1. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  2. 2018Model Cards for Model Reportingcited by 1,068
  3. 2017Selective Classification for Deep Neural Networkscited by 799
  4. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  5. 2018On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Modelscited by 322
  6. 2018Sorting out Lipschitz function approximationcited by 211
  7. 2023Conformal Language Modelingcited by 163
  8. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 52
  9. 2025The Trilemma of Truth in Large Language Modelscited by 6

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