知识的影响和对人类-AI决策中的责任的重要性
Takahiro Tsumura1, Seiji Yamada2
1Faculty of Information Networking for Innovation and Design, Toyo University, Kita-ku, Tokyo, Japan. takahiro.tsumura@iniad.org.
Scientific reports
|December 23, 2025
概括
了解人工智能问责是关键. 对人工智能系统的先验知识将责任转移到人工智能及其开发者身上,特别是在重要任务中,突出了人类-人工智能交互中的动态责任.
科学领域:
- 人与计算机的交互
- 人工智能伦理学 人工智能伦理学
- 认知心理学 认知心理学
背景情况:
- 对人工智能失败的问责至关重要,因为人工智能系统越来越多地支持人类决策.
- 现有关于责任归属的研究重点是系统自主性,透明度或人形化.
- 认知框架 (事先的代理知识) 和上下文框架 (任务重要性) 对责任判断的联合影响未得到充分研究.
研究的目的:
- 调查认知和上下文框架如何共同影响人与代理互动中的责任归因.
- 检查先前对人工智能代理人的知识以及对任务的感知重要性如何影响责任判断.
- 确定在代理人辅助决策中形成问责制差距的因素.
主要方法:
- 一个三要素混合设计实验,涉及588名参与者.
- 参与者评估了模拟失败的代理辅助交互后对用户,代理和开发人员的责任.
- 对因素的操纵包括对代理人的事先了解和感知任务的重要性.
主要成果:
- 之前对人工智能代理的了解显著地将责任从用户转移到代理及其开发者.
- 感知到的任务重要性增加,对开发人员或提供商的责任大幅增加.
- 发现责任分配是动态的,受用户期望和情况严重性的影响.
结论:
- 人与代理互动中的责任不是静态的,而是通过用户对代理的熟悉度和任务的感知重要性来动态调节的.
- 调查结果对设计人工智能系统,教育用户和告知法律政策有影响,以解决问责制缺口.
- 了解这些框架效应对于负责任地部署社会嵌入的AI代理来说至关重要.
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