Current Topological and Machine Learning Applications for Bias Detection in Text
This addresses bias detection in written records for applications like healthcare and education, but it is incremental as it applies existing methods to new data.
This study tackled the problem of detecting bias in text by analyzing the RedditBias database with transformer models like BERT and RoBERTa, finding that BERT (especially mini BERT) excelled in bias classification while multilingual models performed worse.
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.