为了设计以用户为中心的战略建议系统,用于协作的人类-人工智能任务
Lakshita Dodeja1, Pradyumna Tambwekar1, Erin Hedlund-Botti1
1School of Interactive Computing, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
概括
用户的个性影响对人工智能战略建议的偏好. 良心影响选择,更好的调整提高了人工智能系统中感知到的智能和可用性.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 人工智能 (AI) 越来越多地用于复杂的协作任务.
- 个性化推系统在电子商务和社交媒体中很常见.
- 了解用户偏好是有效的人工智能团队合作的关键.
研究的目的:
- 调查以用户为中心的AI战略推系统的因素.
- 测量不同策略建议模式的用户偏好.
- 分析个性对系统选择的影响.
主要方法:
- 人体对象实验有60名参与者.
- 评估了四种推类型:单一,相似,多样化和所有策略.
- 测量用户偏好和系统感知.
主要成果:
- 良心对特定推系统的偏好产生重大影响 (p < 0.01).
- 与用户偏好更高的感知一致性增加了感知智能 (p < 0.01).
- 更大的对齐也导致更高的感知可用性 (p < 0.01).
结论:
- 在设计人工智能战略推系统时,用户个性特征至关重要.
- 系统与用户偏好保持一致,可以提高用户的知觉智能和可用性.
- 这些发现有助于开发更有效的以用户为中心的AI协作工具.
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