Challenges in Annotating Datasets to Quantify Bias in Under-represented
  Society
Vithya Yogarajan Gillian Dobbie Timothy Pistotti Joshua Bensemann Kobe Knowles
Recent advances in artificial intelligence, including the development of
highly sophisticated large language models (LLM), have proven beneficial in
many real-world applications. However, evidence of inherent bias encoded in
these LLMs has raised concerns about equity. In response, there has been an
increase in research dealing with bias, including studies focusing on
quantifying bias and developing debiasing techniques. Benchmark bias datasets
have also been developed for binary gender classification and ethical/racial
considerations, focusing predominantly on American demographics. However, there
is minimal research in understanding and quantifying bias related to
under-represented societies. Motivated by the lack of annotated datasets for
quantifying bias in under-represented societies, we endeavoured to create
benchmark datasets for the New Zealand (NZ) population. We faced many
challenges in this process, despite the availability of three annotators. This
research outlines the manual annotation process, provides an overview of the
challenges we encountered and lessons learnt, and presents recommendations for
future research.