Learning Norms via Natural Language Teachings
This addresses the need for AI to understand social norms for better human interaction, though it appears incremental as it builds on existing debates and computational theories.
The paper tackles the problem of AI systems learning social norms from natural language text, distinguishing between what is normal and normative, and demonstrates a computational approach that enables everyday people to train AI on these concepts.
To interact with humans, artificial intelligence (AI) systems must understand our social world. Within this world norms play an important role in motivating and guiding agents. However, very few computational theories for learning social norms have been proposed. There also exists a long history of debate on the distinction between what is normal (is) and what is normative (ought). Many have argued that being capable of learning both concepts and recognizing the difference is necessary for all social agents. This paper introduces and demonstrates a computational approach to learning norms from natural language text that accounts for both what is normal and what is normative. It provides a foundation for everyday people to train AI systems about social norms.