策略条件化AI使用检测：学术出版的证据框架

中文摘要 AI 辅助

主要学术场所现已发布关于作者、审稿人和领域主席如何使用AI的详细规则，这些规则因角色、任务以及必须披露的内容而异。AI检测，即通常提议用于执行这些规则的工具，估计的是另一件事：文本是否由AI模型撰写。我们认为这一目标与会议和期刊面临的决策不一致，并提出策略条件化AI使用检测，这是一个用于评估人机工作流程是否符合既定规则的证据框架。策略将管理规则作为显式输入。推理报告假设、证据、校准方案和不确定性，而非诸如“检测到AI”之类的结论。评估通过可复现的管道构建基准，这些管道生成合规和不合规的工作流程，并以场所预先设定的假阳性率报告真阳性率。我们通过同行评审来实践该框架，在合理的违规率下，即使在强操作点上的检测器标记的合规作者仍多于违规作者。因此，该框架还指明了场所必须配备的内容：结构化披露、尊重审稿人保密性的已批准工具路由，以及结果可被质疑的途径。在此框架下，检测器不是作者身份分类器，而是一个具有场所预先设定且可辩护的错误率的可审计程序。

英文摘要

Major venues now publish detailed rules about how authors, reviewers, and area chairs may use AI, and those rules differ by role, by task, and by what must be disclosed. AI detection, the instrument usually proposed to enforce them, estimates something else: whether an AI model wrote the text. We argue that this target is misaligned with the decisions conferences and journals face, and propose policy-conditioned AI-use detection, an evidentiary framework for assessing whether a human--AI workflow complied with a stated rule. Policy makes the governing rule an explicit input. Inference reports hypotheses, evidence, calibration regime, and uncertainty in place of verdicts such as "AI detected". Evaluation builds benchmarks from reproducible pipelines that generate compliant and non-compliant workflows, and reports true positive rate at a false positive rate the venue fixes in advance. We work the framework through peer review, where at plausible violation rates a detector at a strong operating point still flags more compliant authors than violating ones. The framework therefore also names what a venue must instrument: structured disclosure, approved-tool routing that respects reviewer confidentiality, and a path by which a finding can be contested. Under this framing a detector is not an authorship classifier but an auditable procedure with an error rate the venue fixes in advance and can defend.

发表机构

• York University（约克大学）

机构由 AI 辅助整理，请以论文原文为准。