作为分布的合规性：基于用途的 AI 监管中的委托合同、错误预算与评估权威分配
Conformance as a Distribution: Delegation Contracts, Error Budgets, and the Allocation of Evaluation Authority in Use-Based AI Regulation
AI 导读
一篇 arXiv cs.AI 论文提出，基于用途的 AI 监管缺少可承载义务的证据对象，应将部署合规视为在声明的上下文分布下的失败率，而非模型基准分数。作者在部署方、审计方与执法点三方策略博弈下给出五组机制与命题，包括用 proper-scoring 惩罚约束部署方超额失败至 O(λ^{-1/2})、从真实流量抽样使审计无偏等，并以五项预注册合成研究验证，未评估任何语言模型或生产系统。
正文 · 原文
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