Culturally-attuned AI: Implicit learning of altruistic cultural values through inverse reinforcement learning.

ABSTRACT: Constructing a universal moral code for artificial intelligence (AI) is challenging because human cultures have different values, norms, and social practices. We therefore argue that AI systems should adapt to culture based on observation: Just as a child raised in a particular culture learns the specific values, norms, and behaviors of that culture, we propose that an AI system operating in a particular human community could similarly learn them as well. How AI systems might accomplish this from observing and interacting with humans has remained an open question. Here, we propose using inverse reinforcement learning (IRL) as a method for AI agents to acquire culturally relevant values implicitly from humans. We test our approach using an experimental paradigm in which AI agents use IRL to

SUBMITTER: Oliveira N 

PROVIDER: S-EPMC12688098 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature