arXiv cs.AI· John deVadoss·· 12 小时前AI 评分24
基于用途的 AI 监管中，合规性应被视为一种分布：委托契约、误差预算与评估权的分配
Conformance as a Distribution: Delegation Contracts, Error Budgets, and the Allocation of Evaluation Authority in Use-Based AI Regulation
AI 导读
研究提出将 AI 部署合规性定义为在声明上下文分布下的失败率，而非模型基准分数，并围绕部署方、审计方与执法点之间的策略博弈给出机制与命题。对声明混合分布的适当评分惩罚可将最优响应部署方的超额失败限制在 O(λ^{-1/2})；基于实时流量的承诺-揭示审计天然无偏；α-investing 可在边际假认证保证下维持长期认证覆盖。五项预注册合成研究验证了这些主张，未评估任何语言模型或生产系统。
正文
Abstract:Use-based AI regulation assigns obligations by deployment risk but lacks an evidentiary object to carry them. A benchmark score is a property of a model; conformance of a deployment is a failure rate under a declared distribution of contexts, bounded by a scope and subject to expiry. We take the delegation contract of earlier work (frozen configuration, scoped claim, executable revision rule) and study it under strategic play between a deployer, an auditor, and an enforcement point. We ask whether a declared context mixture can be made truthful; whether an audit can be unbiased against a system that detects evaluation traffic; whether a certification error budget can sustain a long-lived deployment; whether an aggregate audit can support a per-subgroup guarantee; and whether an incident metric that counts system failures but not fallback costs ranks arrangements correctly. For each we give a mechanism and a proposition: a proper-scoring penalty on the declared mixture bounds the excess failure of a best-responding deployer by $O(\lambda^{-1/2})$ in the penalty weight; a commit-reveal audit drawn from live traffic is unbiased by construction and grades the evaluator with the same sample; a halving error budget drives certification coverage below $(1-p)^n$ while $\alpha$-investing sustains it under a marginal false-certification guarantee; a per-subgroup guarantee costs audit size scaling as $1/m$ in subgroup mass; and any failure-only incident metric prefers withdrawal at every fallback cost, even when total loss ranks the arrangements oppositely. Five preregistered synthetic studies test each claim; no language model or production system is evaluated. Enforcement sits at distribution, capability-access, monetization and risk-transfer choke points, expiry of the conformance record is a standard of care, and conformance-as-a-rate is set against the EU AI Act's static conformity assessment.
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | K.4.1; K.5.2; I.2.6; J.4; G.3 |
| Cite as: | arXiv:2610.33112 [cs.AI] |
| (or arXiv:2610.33112v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33112 arXiv-issued DOI via DataCite |
Submission history
From: John DeVadoss [view email]
[v1]
Fri, 23 Oct 2026 00:08:49 UTC (72 KB)
来源：arXiv cs.AI · arxiv.org