  Institutional bias can impact patient outcomes, educational attainment, and
legal system navigation. Written records often reflect bias, and once bias is
identified; it is possible to refer individuals for training to reduce bias.
Many machine learning tools exist to explore text data and create predictive
models that can search written records to identify real-time bias. However, few
previous studies investigate large language model embeddings and geometric
models of biased text data to understand geometry's impact on bias modeling
accuracy. To overcome this issue, this study utilizes the RedditBias database
to analyze textual biases. Four transformer models, including BERT and RoBERTa
variants, were explored. Post-embedding, t-SNE allowed two-dimensional
visualization of data. KNN classifiers differentiated bias types, with lower
k-values proving more effective. Findings suggest BERT, particularly mini BERT,
excels in bias classification, while multilingual models lag. The
recommendation emphasizes refining monolingual models and exploring
domain-specific biases.
