模型社会偏好自动化车辆行为与道德目标的功能
Chloe Gros1, Leon Kester2, Marieke Martens2,3
1Department Information and Computing Sciences, Universiteit Utrecht Faculteit Betawetenschappen, Utrecht, Netherlands.
Frontiers in artificial intelligence
|December 26, 2025
概括
本研究引入了一项伦理目标函数 (EGF),用于量化自动驾驶汽车 (AV) 决策的社会价值. 融入社会技术反 (SOTEF) 循环的EGF使AV伦理与公众偏好持续保持一致.
科学领域:
- 人工智能伦理学
- 人与计算机的交互
- 伦理哲学的道德哲学
背景情况:
- 自动驾驶汽车 (AV) 需要道德决策框架.
- 现有的框架在很大程度上是概念性的,缺乏经验基础.
- 社会价值观必须指导AV的道德选择.
研究的目的:
- 为AV决策制定一个定量道德目标函数 (EGF).
- 通过使用经验数据,使伦理框架变得可操作.
- 将EGF纳入社会技术反 (SOTEF) 循环,以实现持续的社会调整.
主要方法:
- 与89名荷兰大学生进行的离散选择实验 (DCEs).
- 基于六个伦理突出属性的AV道德场景的评估.
- 多项逻辑 (MNL) 模型来导出代表社会道德偏好的属性权重.
主要成果:
- 该MNL模型显示了稳定的属性权重和63.8%的预测准确度.
- 开发的EGF有效地将社会道德判断转化为计算参数.
- 该研究提供了AV道德框架的第一个经验操作.
结论:
- 根据EGF提供的数据驱动方法,在AV中编码社会价值观.
- 该SOTEF循环促进AV伦理决策的持续改进.
- 未来的研究应该侧重于文化普遍性和多样化的呈现方式.
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