Moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach often relies on language modelling to infer moral relevance or moral properties of a concept such as "smoking". This approach demands complex parameterisation and costly computation, and it tends to disconnect with psychological accounts of moralization. We present a simple cognitive model for moral inference grounded in theories of moralization. Our model builds on word association network known to capture human semantics and draws on rich psychological data. We demonstrate that our moral association graph model performs competitively to state-of-the-art language models, where we evaluate them against a comprehensive set of data for automated inference of moral norms and moral judgment of concepts, and in-context moral inference. Moreover, we show that our model discovers intuitive concepts underlying moral judgment and is applicable to informing short-term temporal changes in moral perception.

Although language is critical to supporting morality within society, it is not clear how moral language itself evolved. We investigate the evolution of moral semantics, hypothesizing that words evolved to take on moral meanings from concrete experiences through metaphorization. We test this hypothesis by analyzing moral semantic change in words from the Moral Foundations Dictionary and the Historical Thesaurus of English over the past hundreds of years. In contrast with the observation that words become concrete over time, we demonstrate that moral words in the English lexicon undergo concrete-to-abstract shifts, reflecting systematic metaphorical mappings to the moral domain. Our results provide large-scale evidence for the role of metaphor in the historical development of the English moral lexicon.

One of the most influential modern theories of morality, Moral Foundations Theory, proposes that morality is formed on innate and shared modular foundations. Psychologists have studied the conceptual development of these moral foundations in childhood, but there exists no comprehensive effort on characterizing the early emergence of moral foundations in naturalistic settings. We explore the emerging order of moral foundations through child and caretaker speech. Using computational methods, we contribute an annotated dataset of moral utterances and find that the individualizing foundations emerge earlier than the binding foundations. Furthermore, caretakers tend to talk more about fairness and degradation, while children talk more about cheating. These results are robust across child gender, family's social class, and race.