Modeling of moral decisions with deep learning

• Main Authors: Christopher Wiedeman, Ge Wang, Uwe Kruger
• Format: Article
• Language: English
• Published: SpringerOpen 2020-11-01
• Series: Visual Computing for Industry, Biomedicine, and Art
• Subjects: Artificial intelligence; Deep learning; Bayesian method; Moral machine experiment
• Online Access: http://link.springer.com/article/10.1186/s42492-020-00063-9

Abstract 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.