IssueBench introduces a comprehensive framework for measuring how language models may subtly bias writing assistance across diverse topics.

• Addresses a critical gap in LLM evaluation by focusing on real-world writing assistance scenarios
• Creates millions of realistic prompts to detect when models favor particular perspectives
• Enables systematic measurement of bias that could influence user thinking
• Provides essential tools for developers to identify and mitigate bias before deployment

This research is crucial for security professionals as it helps identify hidden biases in AI systems that could manipulate public discourse or amplify polarization in sensitive contexts.

IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance