arXiv cs.CY· Li Gan, Eric Gan·· 9 天前AI 评分27
自动化下如何维持人类核查能力：arXiv 论文提出检测目标与专业能力储备框架
Maintaining Human Verification Capacity under Automation
AI 导读
一篇 arXiv 论文将自动化检查依赖、人类核查能力投入与中断时的表现联系起来，指出更好的检查器会降低额外人类技能带来的错误削减收益，从而削弱保留独立专业能力的激励。
正文 · 原文
Abstract:Human verification depends on expertise that must be maintained before it is needed. This paper links reliance on automated checks, investment in human checking ability, and performance during an interruption. Better checking lowers the error reduction gained from an extra unit of human skill while the checker works. It can therefore reduce the incentive to preserve independent expertise, even when it lowers the best achievable expected cost of maintenance and errors. In an illustration, a more informative checker raises detection while it works from \hoPowWorkA{} to \hoPowWorkC{} percent, but the organization then keeps no routine practice and detects \hoPowOnsetC{} percent of errors when the checker first fails, against \hoPowOnsetA{} percent with a less informative checker. A detection requirement therefore concerns both current capability and its survival until new training becomes effective. Evidence from colonoscopy and aviation documents weaker unaided performance under routine automation, without isolating the mechanism. The framework connects a detection target to an explicit reserve of expertise and a training pipeline. Standard detection tests estimate each quantity and reveal automation bias and silent checker failures. The framework distinguishes the requirement from minimizing expected loss and proposes a longitudinal test.
| Subjects: | General Economics (econ.GN); Computers and Society (cs.CY) |
| Cite as: | arXiv:2609.38459 [econ.GN] |
| (or arXiv:2609.38459v1 [econ.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38459 arXiv-issued DOI via DataCite |
Submission history
From: Li Gan [view email]
[v1]
Tue, 29 Sep 2026 19:47:49 UTC (800 KB)
来源：arXiv cs.CY · arxiv.org