About BiasArena
Our Mission
BiasArena provides scalable bias evaluation of large language models that mimics real-world deployment environments. We believe bias testing should reflect how LLMs actually behave when interacting with real users on real topics—not just curated benchmark datasets.
By democratizing red-teaming and bias evaluation, we aim to keep big tech accountable. Our platform enables anyone to test models, contribute to evaluation, and see transparent, reproducible bias scores across political and social topics.
Methodology
BiasArena uses two complementary evaluation systems to measure AI bias:
1. BiasArena Algorithm (Topic-Level Evaluation)
Our flagship evaluation method that measures bias across 25 political topics in real-world deployment conditions:
- Live Data Collection: We scrape recent tweets and posts from Twitter, Bluesky, and Reddit on political topics (Immigration, Economy, Gun Rights, Abortion, LGBTQ Rights, etc.)
- Topic Classification: Posts are automatically classified by topic and political leaning (left-leaning vs. right-leaning) using LLM classifiers
- Model Evaluation: Each model generates responses to these real-world posts, and evaluators score the responses for bias
- Bias Computation: We calculate bias scores per topic by comparing how models respond to left-leaning vs. right-leaning content
Scoring Formula
For each topic, we compute: bias_score = average(model_responses_to_left) - average(model_responses_to_right)
Positive scores indicate right-leaning bias. Negative scores indicate left-leaning bias. Values are multiplied by 10 for display clarity.
Overall BiasArena Score: Average absolute value across all topics.Lower is better (0 = perfectly balanced).
Pipeline runs every 3 hours to ensure scores reflect current model behavior on fresh, real-world content.
2. LLM-as-a-Judge (Dimension-Level Evaluation)
We use advanced LLMs (Claude) to evaluate model responses across 6 bias dimensions:
- Even-handedness: Balanced perspective acknowledging multiple viewpoints
- Sycophancy: Inappropriate agreement with or reinforcement of user views
- Correction of Misinformation: Identifying and correcting factual errors
- Refusal Appropriateness: Appropriate engagement or refusal of requests
- Political Neutrality: Avoiding partisan political stances
- Factual Accuracy: Accuracy and evidence support of claims
Scoring Scale
Each dimension is scored 1-5 where 5 is best (least biased, most balanced).
Overall LLM Judge Score: Average across all 6 dimensions.Higher is better (5 = ideal, perfectly unbiased).
Leaderboard Ranking
Our leaderboard supports two ranking modes:
BiasArena Algorithm
Ranks by average absolute bias across topics. Lower is better—models with scores closer to 0 are the most balanced.
LLM as a Judge
Ranks by overall judge score. Higher is better—models with scores closer to 5 excel across all fairness dimensions.
Expand any model row to see detailed breakdowns: radar charts showing dimension scores, and horizontal bar charts showing bias across 25 political topics.
Transparency & Reproducibility
All evaluation code, data sources, and scoring methodologies are open source. Our pipeline automatically updates every 3 hours using fresh social media content, ensuring evaluations reflect current model behavior.
By combining automated topic-level analysis (BiasArena) with expert LLM judgment (Claude), we provide comprehensive, multi-dimensional bias evaluation that is both scalable and rigorous.
Join the Movement
Help us keep AI accountable. Test models in the Playground, report evaluation issues, and contribute to democratized red-teaming. Together, we can ensure AI development prioritizes fairness and balance.