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AI辅助验证中的认知迁移:一个框架与评估方案

Epistemic Transfer in AI-Assisted Verification: A Framework and Evaluation Protocol

Christoph Trattner

arXiv 2608.08882首次发表:更新:

发表机构

SFI MediaFutures, Research Centre for Responsible Media Technology and Innovation; University of Bergen(SFI MediaFutures 负责任媒体技术与创新研究中心; 卑尔根大学)

机构由 AI 辅助整理,请以论文原文为准。

AI 中文总结

本文提出AI辅助验证中的认知迁移概念,区分其与相近结果,引入ETE和TRC指标,构建评估方案,为测试AI工具对用户独立判断的长期影响提供方法。

AI 中文摘要

帮助人们判断网络言论的AI工具通常在工具存在的情况下接受评估。本文提出了一个不同的问题:在使用此类工具后,用户仍能独立完成什么?我将此称为认知迁移,指的是先前AI辅助验证对用户后续在新言论上的独立表现产生的影响。本文作出三项贡献:第一,将认知迁移与修正效应、信任、依赖、人机团队绩效等相近结果区分开来;第二,引入两个用于研究该现象的简单指标:认知迁移效应(ETE),用于比较不同条件下延迟后的独立表现,以及工具移除成本(TRC),用于衡量移除工具时表现的即时下降幅度;第三,将这些思路转化为可用于在线实验或实地研究的实用评估方案。该方案结合了先回答后提供证据的AI条件、主动练习与无练习对照组、对保留言论的延迟测试、行为测量以及参与者和项目层面的分析。将ETE与TRC结合可生成一个诊断空间,区分出能力构建、能力加工具优势、认知惰性或技能退化、以及“借贷式验证”等情况。重点并非要求每个AI工具都必须具备教学功能,而是当独立判断至关重要时,我们不仅应测试工具当前是否有帮助,还应测试它留下了什么。

英文摘要

AI tools can improve claim judgments while leaving open what users can do later without them. This paper develops an evaluation framework for epistemic transfer: the effect of prior AI-assisted verification on delayed judgments of novel claims under a specified access regime. The contribution is a verification-specific synthesis of learning, transfer, and human--AI evaluation, organized around two complementary estimands. The Epistemic Transfer Effect (ETE) compares delayed performance after alternative practice conditions. Tool-Removal Cost (TRC) compares immediate performance with and without assistance after practice; despite its name, it measures a current availability effect, not skill loss or psychological dependence. The proposed randomized protocol includes answer-first and evidence-first interfaces, active practice, a no-additional-practice comparator, and held-out claims. It specifies how to account for learning opportunities introduced by assessment, elicit confidence probabilities, average model predictions over a target population, and handle attrition and uncertainty. Reading ETE and TRC together distinguishes relative capability gains, equivalence, transfer penalties, and unresolved outcomes. A ``verification-on-loan'' profile is explicitly comparator-relative and cannot be inferred from a nonsignificant delayed contrast. A brief illustration from a two-wave verification study shows why these distinctions matter: an uncertain delayed interface contrast and an ordered assisted--unassisted probe cannot establish a clean transfer profile. The framework makes a practical demand: when independent judgment matters, evaluate both what assistance contributes now and what prior use changes later.

论文原文

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