From Bytes to Biases: Investigating the Cultural Self-Perception of Large Language Models

TL;DR

This paper investigates the cultural self-perception of large language models like ChatGPT and Bard, revealing biases towards English-speaking and economically competitive countries, highlighting the importance of understanding AI biases.

Contribution

It introduces a novel approach to assess LLMs' cultural biases using value questions from the GLOBE project, providing insights into their cultural self-perception.

Findings

LLMs' cultural self-perception aligns with English-speaking countries.

Biases favor economically competitive nations.

Understanding these biases is crucial for societal AI deployment.

Abstract

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.