探索用于量化人工智能利用的投资结构
Ulubilge Ulusoy1, Garrett E Reisman2
1University of Colorado Boulder, USA.
Human factors
|February 24, 2026
概括
投资框架通过测量机会成本来量化人类人工智能使用情况. 较低的任务负载增加了人工智能投资,从而影响了主观投资.
科学领域:
- 人与人工智能的互动
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 投资构造提供了一种新的方法来量化人类对人工智能代理的利用.
- 它基于人类用户所承受的可衡量的机会成本.
- 一项实验研究旨在使这种构造成为可操作的,并调查其影响因素.
研究的目的:
- 通过投资结构量化人类对人工智能的利用.
- 检查影响人工智能利用的尊重,信任,工作量和自信等因素.
- 在实际的人类-人工智能协作场景中验证投资框架.
主要方法:
- 一项实验性研究涉及30名参与者在人工智能协助下执行维护任务.
- 参与者被分为三组,任务完成时间有所不同,以模拟不同的任务负载.
- 收集了投资的客观 (类型数据) 和主观 (定制规模) 措施,以及影响因素的数据.
主要成果:
- 在任务负载组之间观察到投资措施的显著差异,较低的任务负载导致投资增加.
- 投资的客观和主观指标显示出正相关性.
- 在尊重和投资的主观衡量标准之间发现了相关性.
结论:
- 这项研究为投资框架提供了第一个经验支持,通过机会成本对人工智能利用进行概念化.
- 这些发现表明,任务负载会影响人类人工智能投资.
- 尊重成为与主观AI利用相关的重要因素.
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