From Bytes to Biases: Investigating the Cultural Self-Perception of
  Large Language Models
Wolfgang Messner Tatum Greene Josephine Matalone
Large language models (LLMs) are able to engage in natural-sounding
conversations with humans, showcasing unprecedented capabilities for
information retrieval and automated decision support. They have disrupted
human-technology interaction and the way businesses operate. However,
technologies based on generative artificial intelligence (GenAI) are known to
hallucinate, misinform, and display biases introduced by the massive datasets
on which they are trained. Existing research indicates that humans may
unconsciously internalize these biases, which can persist even after they stop
using the programs. This study explores the cultural self-perception of LLMs by
prompting ChatGPT (OpenAI) and Bard (Google) with value questions derived from
the GLOBE project. The findings reveal that their cultural self-perception is
most closely aligned with the values of English-speaking countries and
countries characterized by sustained economic competitiveness. Recognizing the
cultural biases of LLMs and understanding how they work is crucial for all
members of society because one does not want the black box of artificial
intelligence to perpetuate bias in humans, who might, in turn, inadvertently
create and train even more biased algorithms.