LLM BiasScope: A Real-Time Bias Analysis Platform for Comparative LLM Evaluation

Researchers Himel Ghosh and Nick Elias Werner have developed LLM BiasScope, a comprehensive open-source web platform that enables real-time, comparative bias analysis of large language models. The system supports seven major AI providers including Google Gemini, Meta Llama, Mistral, DeepSeek, MiniMax, Meituan, and more, allowing users to test the same prompts across different models simultaneously. Built with React on this http URL and leveraging the Vercel AI SDK for multi-provider access, the platform features synchronized streaming responses where users can watch two models generate text side-by-side while bias detection runs automatically.

LLM BiasScope employs a sophisticated two-stage bias detection pipeline: first identifying biased sentences, then classifying them into specific bias types using Hugging Face inference endpoints. The interface provides per-model bias summaries, comparison views highlighting distribution differences, and interactive visualizations including bar charts and radar plots. Users can export complete analysis results to JSON or PDF formats for documentation and further study. The platform represents a significant step toward standardized bias evaluation, moving beyond anecdotal testing to systematic, data-driven comparison of how different LLMs handle sensitive topics.

The tool was accepted for presentation at the prestigious EACL 2026 conference (March 24-29 in Morocco), indicating its academic rigor and practical value. By making bias analysis accessible through a web interface rather than requiring complex coding setups, LLM BiasScope lowers the barrier for researchers, developers, and organizations to conduct thorough bias audits. The real-time streaming capability combined with detailed statistical breakdowns allows for both quick qualitative assessments and deep quantitative analysis of model behavior patterns.