Upholding Epistemic Agency: A Brouwerian Assertibility Constraint for Responsible AI
- URL: http://arxiv.org/abs/2603.03971v1
- Date: Wed, 04 Mar 2026 12:14:21 GMT
- Title: Upholding Epistemic Agency: A Brouwerian Assertibility Constraint for Responsible AI
- Abstract summary: I propose a Brouwer-inspired assertibility constraint for responsible AI.<n>In high-stakes domains, systems may assert or deny claims only if they can provide a publicly inspectable and contestable certificate of entitlement.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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) that 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 forcing witness is available. By making outputs answerable to challengeable warrants rather than confidence alone, the paper aims to preserve epistemic agency where automated speech enters public justification.
Related papers
- Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineering [78.51705689800838]
We propose an assurance-revision loop that uses deployment evidence to revise the requirements, model, or evaluator.<n>We then cast assured agentic development as a resource-allocation problem over human judgment, agent capability, and compute.
arXiv Detail & Related papers (2026-09-10T17:58:48Z) - Stored Is Not Supported: Typed Provenance and Assertion Guardrails for Persistent AI Agents [2.124730017640531]
Persistent AI agents construct autobiographical state through reflection, retrieval, and consolidation.<n>We specify typed provenance and assertion guardrails for autobiographical assertion boundedness.
arXiv Detail & Related papers (2026-09-02T05:35:33Z) - GoodDiffusion: Proactive Copyright Protection for Diffusion Bridge Models via Learnable Sample-specific Signatures [88.63093209858387]
We propose GoodDiffusion, inspired by backdoor mechanisms, to enforce model-level use-time control.<n>GoodDiffusion preserves high-quality generation for authorized queries carrying valid signatures, yet refuses to generate for unauthorized inputs.<n>To strengthen security, we introduce a Learnable Signature Network (LSN) that assigns sample-specific signatures conditioned on each input.
arXiv Detail & Related papers (2026-06-29T04:07:09Z) - Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation [38.81368744366676]
Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic representation of uncertainty.<n>We propose Belief-Augmented Generation (BAG): grounding LLMs in their own belief state via the prompt and letting them reason over these K samples to decide on a conversational strategy.<n>BAG improves QA accuracy across six models and yields strategy decisions more faithful to the belief state than prompt-only baselines.
arXiv Detail & Related papers (2026-05-25T13:29:02Z) - Interpretation, Learning, and Empathy as One Constraint: A Residual-Adequacy Architecture with Accountable Abstention [0.0]
We develop a small cognitive architecture in which limits arise from a single quantity.<n>An Interpretation-Decision Unit (IDU) interprets a content vector through a family of regimes and decides which actions it licenses.<n>We prove the unit is total and deterministic: for any content and fixed configuration it halts in finitely many bounded-cost steps with a unique terminal witness.
arXiv Detail & Related papers (2026-05-24T10:57:28Z) - Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems [2.124730017640531]
We introduce a verification framework for governed mutation systems that computes execution authority from structured artifacts.<n>DTF provides an infrastructure foundation for making agentic execution governable, auditable, and bounded in sovereign AI deployments.
arXiv Detail & Related papers (2026-05-13T17:58:52Z) - The Open-Box Fallacy: Why AI Deployment Needs a Calibrated Verification Regime [0.5277024349608833]
We argue that authorization should be domain-scoped, independently checkable, monitored after release, accountable, contestable, and revocable.<n>We propose Verification Coverage, a six-component reportable standard with a minimum-composition rule, as the metric that should sit beside capability scores in model cards, leaderboards, and regulatory disclosures.
arXiv Detail & Related papers (2026-05-11T14:02:56Z) - Towards Selection as Power: Bounding Decision Authority in Autonomous Agents [0.0]
We propose a governance architecture that separates cognition, selection, and action into distinct domains and models autonomy as a vector of sovereignty.<n>We evaluate the system across multiple regulated financial scenarios under adversarial stress targeting variance manipulation, threshold gaming, framing skew, ordering effects, and entropy probing.<n>Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity.
arXiv Detail & Related papers (2026-02-16T10:10:47Z) - ADEPT: RL-Aligned Agentic Decoding of Emotion via Evidence Probing Tools -- From Consensus Learning to Ambiguity-Driven Emotion Reasoning [67.22219034602514]
We introduce ADEPT (Agentic Decoding of Emotion via Evidence Probing Tools), a framework that reframes emotion recognition as a multi-turn inquiry process.<n> ADEPT transforms an SLLM into an agent that maintains an evolving candidate emotion set and adaptively invokes dedicated semantic and acoustic probing tools.<n>We show that ADEPT improves primary emotion accuracy in most settings while substantially improving minor emotion characterization.
arXiv Detail & Related papers (2026-02-13T08:33:37Z) - interwhen: A Generalizable Framework for Verifiable Reasoning with Test-time Monitors [47.363850513075356]
We present a test-time verification framework, interwhen, that ensures that the output of a reasoning model is valid wrt. a given set of verifiers.<n> Verified reasoning is an important goal in high-stakes scenarios such as deploying agents in the physical world.
arXiv Detail & Related papers (2026-02-05T08:35:01Z) - EigenAI: Deterministic Inference, Verifiable Results [3.231493128964671]
EigenAI is a verifiable AI platform built on top of the EigenLayer restaking ecosystem.<n>It combines a deterministic large-language model (LLM) inference engine with a cryptoeconomically secured optimistic re-execution protocol.
arXiv Detail & Related papers (2026-01-30T04:46:22Z) - Preventing the Collapse of Peer Review Requires Verification-First AI [49.995126139461085]
We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
arXiv Detail & Related papers (2026-01-23T17:17:32Z) - The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems [0.0]
We show modern CI/CD pipelines exhibit a structural failure in responsibility attribution.<n>We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide.<n>We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch- or system-level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.
arXiv Detail & Related papers (2026-01-21T15:05:27Z) - The Epistemic Suite: A Post-Foundational Diagnostic Methodology for Assessing AI Knowledge Claims [0.7233897166339268]
This paper introduces the Epistemic Suite, a diagnostic methodology for surfacing the conditions under which AI outputs are produced and received.<n>Rather than determining truth or falsity, the Suite operates through twenty diagnostic lenses to reveal patterns such as confidence laundering, narrative compression, displaced authority, and temporal drift.
arXiv Detail & Related papers (2025-09-20T00:29:38Z) - COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
arXiv Detail & Related papers (2025-06-25T07:04:49Z) - Latent Veracity Inference for Identifying Errors in Stepwise Reasoning [78.29317733206643]
We introduce Veracity Search (VS), a discrete search algorithm over veracity assignments.<n>It performs otherwise intractable inference in the posterior distribution over latent veracity values.<n>It generalizes VS, enabling accurate zero-shot veracity inference in novel contexts.
arXiv Detail & Related papers (2025-05-17T04:16:36Z) - The Decisive Power of Indecision: Low-Variance Risk-Limiting Audits and Election Contestation via Marginal Mark Recording [51.82772358241505]
Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections.
We define new families of audits that improve efficiency and offer advances in statistical power.
New audits are enabled by revisiting the standard notion of a cast-vote record so that it can declare multiple possible mark interpretations.
arXiv Detail & Related papers (2024-02-09T16:23:54Z) - Weakly Supervised Learners for Correction of AI Errors with Provable
Performance Guarantees [38.36817319051697]
We present a new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees.
These AI correctors are auxiliary maps whose role is to moderate the decisions of some previously constructed underlying classifier by either approving or rejecting its decisions.
A key technical focus of the work is in providing performance guarantees for these new AI correctors through bounds on the probabilities of incorrect decisions.
arXiv Detail & Related papers (2024-01-31T20:36:13Z) - Auditing AI models for Verified Deployment under Semantic Specifications [65.12401653917838]
AuditAI bridges the gap between interpretable formal verification and scalability.
We show how AuditAI allows us to obtain controlled variations for verification and certified training while addressing the limitations of verifying using only pixel-space perturbations.
arXiv Detail & Related papers (2021-09-25T22:53:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.