Benchmarking the Ethics of Large Language Models with Polarizing Topics

The increasing deployment of artificial intelligence in various domains has raised critical questions about the ethical implications of large language models, particularly in politically sensitive contexts. Benchmarking the ethical performance of these models is both novel and significant because it provides a systematic approach to evaluating their handling of politically polarizing topics. This research focuses on Google Gemini and Anthropic Claude, utilizing an automated framework to perform sentiment analysis, bias detection, and ethical evaluation without human intervention. The analysis revealed that while both models exhibit strengths in fairness and transparency, they still demonstrate moderate levels of political bias, showing the need for ongoing refinement of ethical evaluation techniques. The study's findings highlight the importance of rigorous ethical benchmarks in ensuring that AI systems operate within ethical boundaries, thereby fostering greater trust and accountability. The proposed framework offers a scalable and reproducible method for ethical evaluation, contributing valuable insights to the field of AI ethics.