Abstract
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.
Cite this work
Cecilia Delgado Solorzano and Carlos Toxtli-Hernández. 2023. Evaluating Machine Perception of Indigeneity: An Analysis of ChatGPT's Perceptions of Indigenous Roles in Diverse Scenarios. Workshop on Conceptualizing Indigeneity in Social Computing, ACM CSCW 2023, Minneapolis, USA. https://doi.org/10.13140/RG.2.2.30617.39520
@misc{Delgado2023Evaluating,
doi = {10.13140/RG.2.2.30617.39520},
url = {https://www.researchgate.net/doi/10.13140/RG.2.2.30617.39520},
author = {Delgado, Cecilia and Toxtli, Carlos},
language = {en},
title = {Evaluating Machine Perception of Indigeneity: An Analysis of ChatGPT's Perceptions of Indigenous Roles in Diverse Scenarios},
publisher = {Unpublished},
year = {2023},
howpublished = {Preprint, ResearchGate},
note = {Preprint}
}Related
