奖励和惩罚有助于人类克服对被认为是机器的合作伙伴的偏见
Kinga Makovi1, Jean-François Bonnefon2, Mayada Oudah1
1Social Science Division, New York University Abu Dhabi, Abu Dhabi, UAE.
iScience
|July 15, 2025
概括
由于"机器处罚",人们与人工智能合作伙伴的合作较少. 结合同行奖励和惩罚,有效地消除了这种惩罚,促进了人工智能开发的更好的人机合作.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 行为经济学是一种行为经济学.
背景情况:
- 有效的人机合作对于利用人类和人工智能 (AI) 的互补优势至关重要.
- 一个重要的障碍是"机器惩罚",人类与人类合作伙伴相比,与被认为是人工智能合作伙伴的合作减少.
- 了解和减轻这种惩罚对于成功地将AI整合到协作任务中至关重要.
研究的目的:
- 调查在人类-人工智能协作中克服机器惩罚的策略.
- 为了确定同行奖励和惩罚的有效性,单独和组合,在促进与AI合作.
- 为设计适合人与人工智能交互的合作环境提供见解.
主要方法:
- 进行了一场大规模的,代的公共产品游戏,涉及近2000名参与者.
- 参与者被引导相信他们的伴侣是人还是机器.
- 实施了同行奖励和同行惩罚机制,无论是独立的还是联合的.
主要成果:
- 同行奖励和同行惩罚都单独增加了与假定机器合作伙伴的合作.
- 无论是同行奖励还是同行惩罚,都不能单独成功地消除机器惩罚.
- 同行奖励和惩罚的组合独特地消除了机器惩罚,对机器合作伙伴有效,对人类合作伙伴高效.
结论:
- 同行奖励和惩罚的综合系统是克服机器惩罚的可行策略.
- 这种双重方法增强了人类-人工智能团队的合作,而不会影响人与人互动的效率.
- 这些发现为设计基于特定目标的合作人机系统提供了细微的框架.
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