Large Language Models (LLMs), like ChatGPT, are fundamentally tools trained on vast data, reflecting diverse societal impressions. This paper aims to investigate LLMs' self-perceived bias concerning indigeneity when simulating scenarios of indigenous people performing various roles. Through generating and analyzing multiple scenarios, this work offers a unique perspective on how technology perceives and potentially amplifies societal biases related to indigeneity in social computing. The findings offer insights into the broader implications of indigeneity in critical computing.

翻译：大型语言模型（LLMs），如ChatGPT，本质上是基于海量数据训练的工具，反映了多元的社会印象。本文旨在探究LLMs在模拟土著人扮演不同角色的场景时，对土著性的自我感知偏见。通过生成并分析多个场景，本研究提出了一个独特视角，揭示技术在社会计算中如何感知并可能放大与土著性相关的社会偏见。研究结果对于理解土著性在关键计算领域的广泛影响提供了深刻见解。