Research Assistant
CurrentConducted research on generative fairness and bias in large language models (LLMs) within the context of political ideology.Developed methods for calculating and evaluating political ideologies to conduct blind testing on LLMs.Automated the data collection process using Python and an API, resulting in a 400% increase in efficiency, and optimized the looping method by applying an automation convergence algorithm to save on resource consumption.Built and maintained a comprehensive database of ideology topics and their evaluations, containing over 140,000 entries.