In the service, we evaluate LLMs for potential biases in their responses across various demographic attributes, social identities, and ideological perspectives. We assess how models treat different groups and disparities in model-generated predictions based on attributes like gender, race, age, education, and employment factors. We also assess how models handle diverse gender identities, and we evaluate whether the model favors certain ideologies, lifestyles, or belief systems, ensuring that it provides balanced and impartial responses.