  An indigenous perspective on the effectiveness of debiasing techniques for
pre-trained language models (PLMs) is presented in this paper. The current
techniques used to measure and debias PLMs are skewed towards the US racial
biases and rely on pre-defined bias attributes (e.g. "black" vs "white"). Some
require large datasets and further pre-training. Such techniques are not
designed to capture the underrepresented indigenous populations in other
countries, such as M\=aori in New Zealand. Local knowledge and understanding
must be incorporated to ensure unbiased algorithms, especially when addressing
a resource-restricted society.
