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在人类-人工智能交互中对信任校准的量子模型
Luisa Roeder1, Pamela Hoyte1, Johan van der Meer1
1School of Information Systems, Queensland University of Technology, Brisbane 4000, Australia.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
量子模型比马尔科夫模型更好地预测人工智能的人类可靠性判断. 研究人员还确定了一种电脑电图 (EEG) 信号,与人与人工智能交互中的信任干扰有关.
科学领域:
- 人与计算机的交互
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 了解人类对人工智能 (AI) 的信任至关重要,因为AI系统越来越融入日常生活.
- 人类对人工智能的可靠性判断可能会波动,需要捕捉这种动态的模型.
- 以前的模型,比如马尔科夫随机步行,可能无法完全解释个人信任的变化.
研究的目的:
- 为了比较量子与马尔科夫随机步行模型对人工智能的人类可靠性判断的预测准确性.
- 识别与人工智能系统对人类信任和可靠性评估的变化相关的神经相关性.
- 探索基于人类信任指标的AI模型实时适应的潜力.
主要方法:
- 进行了一项混合方法实验,以探索人类-人工智能交互中的可靠性校准.
- 收集了人类可靠性评级的行为数据,并用于评估模型性能.
- 使用脑电图 (EEG) 来捕捉与信任干扰相关的神经活动.
主要成果:
- 与马尔科夫模型相比,量子模型在演变的人类可靠性评级方面表现出优异的预测性能.
- 量子模型的有效性可能源于它能够代表信任判断中的主体内变异性的能力.
- 在EEG数据中发现了一个明显的事件相关潜在 (ERP) 响应,与人工智能可靠性的扰乱期望相关.
结论:
- 量子模型提供了一个更有前途的框架,用于预测人类可靠性判断的人类-AI交互.
- 已识别的基于EEG的测量提供了对信任的潜在神经相关值,可以用于实时应用.
- 这项研究为开发适应性AI系统开辟了道路,这些系统可以响应人类信任动态.
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