人工智能算法的偏好:在不确定性下的自动化决策中需要相关的风险态度
1Department of Management Science and Engineering, Stanford University, Stanford, California, USA.
概括
人工智能 (AI) 可以帮助决策,但可能会嵌入与实际用户不一致的风险态度. 人工智能系统的透明度和可调节的风险偏好对于准确的决策支持至关重要.
科学领域:
- 决策科学 决策科学 决策科学
- 人工智能的人工智能
- 风险管理 风险管理
背景情况:
- 人工智能 (AI) 通过数据分析和自动建议,为降低风险和改进决策提供了巨大的潜力.
- 规范性AI算法面临挑战,特别是当数据不完整或不准确时,以及在不确定性下结合风险态度时.
- 当人工智能系统中嵌入的风险态度不准确地反映最终用户或受决策影响者的偏好时,就会出现一个关键问题.
研究的目的:
- 突出决策者的风险偏好与AI系统中编程的风险态度之间的差异.
- 倡导人工智能决策支持工具中对风险参数进行更大的透明度和用户控制.
主要方法:
- 在不确定性下对AI决策过程的概念分析.
- 探索AI算法中风险态度差异的来源.
- 制定简化案例研究,说明所发现的问题.
主要成果:
- 人工智能算法本质上嵌入了风险态度,这可能与实际决策者的偏好不一致.
- 这种错位可能会导致低于最佳或不适当的决策,特别是在医学等关键领域和竞争性帆船等复杂操作中.
- 选择选项和专家投入的过程影响了嵌入式风险态度.
结论:
- 确保人工智能决策与用户的风险偏好保持一致,需要使人工智能风险因素透明和可调节.
- 建议包括提高AI参数的可访问性,并在模型中实现用户驱动的偏好调整.
- 解决这种差异对于人工智能在风险管理和决策支持中的负责任和有效部署至关重要.
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