影响对算法决策信任的因素:一个基于场景的间接实验
Fernando Marmolejo-Ramos1, Rebecca Marrone2, Malgorzata Korolkiewicz2
1College of Education, Psychology, and Social Work, Flinders University, Adelaide, SA, Australia.
Frontiers in artificial intelligence
|February 19, 2025
概括
在低风险与高风险决策中,统计素养对算法的信任有不同的影响. 可解释性没有影响信任,这表明需要更广泛的AI识字教育.
科学领域:
- 决策科学 决策科学 决策科学
- 人与计算机的交互
- 人工智能伦理学 人工智能伦理学
背景情况:
- 尽管在决策中广泛使用算法,但公众对算法的不信任仍然很普遍.
- 假设教育干预,例如解释算法过程,可以减少这种不信任.
研究的目的:
- 调查统计素养,算法可解释性和对算法的信任之间的关系.
- 检查这些因素在低风险和高风险决策环境中如何不同.
主要方法:
- 这项研究涉及20个国家的1921名参与者.
- 对算法对低风险和高风险决策的信任分析.
- 评估统计素养和算法可解释性对信任水平的影响.
主要成果:
- 统计素养与对高风险算法决策的信任有负面关联.
- 统计素养与对使用熟悉算法的低风险场景的信任有积极的相关性.
- 算法可解释性并没有显著影响参与者对算法的信任.
结论:
- 统计素养使个人能够批判性地评估算法输出,特别是在重大决策方面.
- 促进统计和人工智能 (AI) 识字对于导航算法信任的复杂性至关重要.
- 未来的研究应该探索用户交互和生理措施,以更准确地评估信任.
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