# Evaluating Machine Perception of Indigeneity: An Analysis of ChatGPT's Perceptions of Indigenous Roles in Diverse Scenarios

arXiv: https://arxiv.org/abs/2310.09237 · HTML: https://mlsanity.com/paper/2310.09237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [AI and the Everything in the Whole Wide World Benchmark](https://mlsanity.com/paper/2111.15366/) (2021)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)

## Built on by (most cited first, 2 of 2)

- [Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem](https://mlsanity.com/paper/2406.15625/) (2024)
- [Laissez-Faire Harms: Algorithmic Biases in Generative Language Models](https://mlsanity.com/paper/2404.07475/) (2024)

## Abstract (arXiv)

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.