The high dimensional psychological profile and cultural bias of ChatGPT
<|reference_start|>The high dimensional psychological profile and cultural bias of ChatGPT: Given the rapid advancement of large-scale language models, artificial intelligence (AI) models, like ChatGPT, are playing an increasingly prominent role in human society. However, to ensure that artificial intelligence models benefit human society, we must first fully understand the similarities and differences between the human-like characteristics exhibited by artificial intelligence models and real humans, as well as the cultural stereotypes and biases that artificial intelligence models may exhibit in the process of interacting with humans. This study first measured ChatGPT in 84 dimensions of psychological characteristics, revealing differences between ChatGPT and human norms in most dimensions as well as in high-dimensional psychological representations. Additionally, through the measurement of ChatGPT in 13 dimensions of cultural values, it was revealed that ChatGPT's cultural value patterns are dissimilar to those of various countries/regions worldwide. Finally, an analysis of ChatGPT's performance in eight decision-making tasks involving interactions with humans from different countries/regions revealed that ChatGPT exhibits clear cultural stereotypes in most decision-making tasks and shows significant cultural bias in third-party punishment and ultimatum games. The findings indicate that, compared to humans, ChatGPT exhibits a distinct psychological profile and cultural value orientation, and it also shows cultural biases and stereotypes in interpersonal decision-making. Future research endeavors should emphasize enhanced technical oversight and augmented transparency in the database and algorithmic training procedures to foster more efficient cross-cultural communication and mitigate social disparities.<|reference_end|>
@article{yuan2024the,
    title={The high dimensional psychological profile and cultural bias of ChatGPT},
    author={Hang Yuan (1), Zhongyue Che (1), Shao Li (1), Yue Zhang, Xiaomeng Hu
  (2), and Siyang Luo (1) ((1) Sun Yat-Sen University, (2) Renmin University of
  China)},
    journal={arXiv preprint arXiv:2405.03387},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03387},
    primaryClass={cs.CL}
}