在自动驾驶汽车的伦理困境中应用和设计决策模型
Guoman Liu1, Jing Sheng2, Zhen Tao2
1Jiangxi Province Key Laboratory of Precision Drive and Equipment, Nanchang Institute of Technology, Nanchang, 330099, China. jerry@nit.edu.cn.
Scientific reports
|March 11, 2025
概括
一个新的计算模型,ACWADOE,通过结合人类偏好来改善自动驾驶汽车的伦理决策. 这种人工智能模型在复杂的道德困境中达到92.5%的准确性,提高了对自动驾驶汽车的信任.
科学领域:
- 人工智能的人工智能
- 自主系统 自主系统
- 计算伦理学 计算伦理学
背景情况:
- 自动驾驶汽车面临着道德困境,导致由于决策不清楚导致公众不信任.
- 现有的道德框架与自动驾驶汽车的复杂,现实世界的伦理场景作斗争.
研究的目的:
- 在道德困境中开发自动驾驶汽车的计算,多因素决策模型.
- 通过与人类偏好保持一致,增强自动驾驶汽车的令人满意和明确的决策.
主要方法:
- 拟议的ACWADOE (基于属性相关性的WADOE) 模型.
- 使用道德机器调查数据计算先前的概率,以反映人类的倾向.
- 通过设计的困境来确定因子相关性和信息获取比率.
- 使用对对比矩阵计算因子权重.
- 通过40个测试困境与NB,ADOE和WADOE进行验证ACWADOE.
主要成果:
- 与其他模型相比,ACWADOE证明了对人类需求的卓越适应性.
- 在道德困境解决方面取得了92.5%的高准确率.
- 该模型有效地整合了影响因素,以便更细致的决策.
结论:
- ACWADOE为自动驾驶汽车的伦理决策提供了一个强大的计算框架.
- 该模型通过与人类的道德偏好保持一致来增强对自主系统的信任.
- 为其他面临伦理挑战的AI系统提供参考.
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