Evaluating Machine Perception of Indigeneity: An Analysis of ChatGPT's Perceptions of Indigenous Roles in Diverse Scenarios
This work addresses bias in AI for indigenous communities, but it is incremental as it focuses on analyzing existing models without introducing new methods.
The paper investigated ChatGPT's self-perceived bias regarding indigeneity by simulating scenarios of indigenous people in various roles, finding that the model reflects and potentially amplifies societal biases in social computing.
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.