Policy-Conditioned AI-Use Detection: An Evidentiary Framework for Academic Publishing

2026-09-29

AI Summary

This study addresses the misalignment between existing AI detection methods, which merely classify text provenance, and the compliance requirements of academic publishing. We propose a policy-conditioned evidence framework that treats publication rules as explicit inputs to evaluate the compliance of human-AI collaborative workflows, replacing simplistic binary "AI-generated" classification. Methodologically, this work innovatively reframes the detection objective from authorship attribution to auditable compliance procedures, explicitly defining error rate thresholds and dispute resolution mechanisms. Technically, it employs reproducibility-based pipeline benchmarking and evaluates true positive rates at fixed false positive rates. Validation in peer review scenarios demonstrates that even high-precision systems may misclassify compliant authors, underscoring the necessity for supporting infrastructure such as structured disclosure mechanisms.

Abstract

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.

Problem

• AI-use detection
• academic publishing policy
• misalignment
• peer review
• evidentiary framework

Innovation

• Policy-Conditioned Detection
• Evidentiary Framework
• AI-Use Compliance
• Auditable Procedure
• Peer Review