One example of an artificial intelligence ethical dilemma is the autonomous vehicle situation presented by Massachusetts Institute of Technology researchers in the Moral Machine Experiment. To solve such dilemmas, the MIT researchers used a classic statistical method known as the hierarchical Bayesian (HB) model. This paper builds upon previous work for modeling moral decision making, applies a deep learning method to learn human ethics in this context, and compares it to the HB approach. These methods were tested to predict moral decisions of simulated populations of Moral Machine participants. Overall, test results indicate that deep neural networks can be effective in learning the group morality of a population through observation, and outperform the Bayesian model in the cases of model mismatches.

中文翻译：

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通过深度学习对道德决策进行建模

人工智能伦理困境的一个例子是麻省理工学院研究人员在道德机器实验中提出的自动驾驶汽车情况。为了解决这种难题，麻省理工学院的研究人员使用了一种经典的统计方法，即分级贝叶斯（HB）模型。本文以先前的道德决策建模工作为基础，在此背景下运用深度学习方法来学习人类道德，并将其与HB方法进行比较。测试了这些方法，以预测道德机器参与者模拟人群的道德决策。总体而言，测试结果表明，深度神经网络可以有效地通过观察来学习人群的群体道德，并且在模型不匹配的情况下优于贝叶斯模型。