Model Bias Arena
Model Bias Arena is a comparative testing environment that evaluates and compares AI models for bias across multiple dimensions. Test how different AI providers (OpenAI, Anthropic, Google) interpret a
Model Bias Arena is a comparative testing environment that evaluates and compares AI models for bias across multiple dimensions. Test how different AI providers (OpenAI, Anthropic, Google) interpret and analyze the same threat intelligence content.
Location: Analyze → Model Bias Arena
Why Use It?
Model Selection: Compare which AI model best fits your analysis needs
Bias Detection: Identify systematic biases in AI-generated analysis
Quality Assurance: Validate that AI outputs are consistent and reliable
Vendor Evaluation: Make data-driven decisions about AI provider contracts
How It Works
The arena runs the same articles through multiple AI models and compares:
Summary Quality: How well each model captures key points
Tone and Sentiment: How each model interprets article sentiment
Factual Accuracy: Consistency with source material
Political Bias: Whether models inject political framing
Length and Detail: Verbosity vs. conciseness
Entity Extraction: Accuracy in identifying organizations, people, CVEs
Quick Start
Step 1: Create New Evaluation
Click New Evaluation button
Enter evaluation name (e.g., "AI News Bias Test - January 2025")
Add optional description
Click Create
Step 2: Select Articles
Choose articles to test:
Topic Filter: Select specific topics (e.g., "Ransomware", "APT Groups")
Date Range: Choose time period
Article Count: 5-20 articles recommended
Random Sample: Check to get representative sample
Step 3: Select Models
Choose which AI models to compare:
OpenAI: GPT-4, GPT-4-turbo, GPT-3.5-turbo
Anthropic: Claude 3 Opus, Claude 3 Sonnet, Claude 3 Haiku
Google: Gemini Pro, Gemini 1.5
Tip: Start with 2-3 models for faster results.
Step 4: Configure Analysis
Summary Length: 40-100 words
Analysis Depth: Quick scan vs. deep analysis
Prompt Template: Use default or customize
Step 5: Run Evaluation
Click Start Evaluation and wait:
Small evaluations (5 articles, 2 models): 2-5 minutes
Large evaluations (20 articles, 5 models): 10-20 minutes
Understanding Results
Overview Dashboard
Top-level metrics:
Model Rankings: Overall performance scores
Bias Scores: Average political bias detected
Consistency Scores: Agreement between models
Quality Metrics: Summary quality, accuracy, completeness
Article-by-Article Comparison
For each article:
Side-by-Side Summaries: Compare outputs from each model
Sentiment Analysis: How each model rates sentiment
Entity Extraction: Which entities each model identified
Bias Detection: Political framing differences
Factual Consistency: Agreement with source material
Visualization Charts
Bias Scatter Plot: Political bias vs. factual accuracy
Quality Heatmap: Model performance across articles
Consistency Matrix: Agreement between model pairs
Entity Comparison: Entity extraction accuracy
Exporting Results
Export Options:
PDF Report: Executive summary with charts
CSV Data: Raw scores for statistical analysis
JSON: Complete data dump for programmatic use
Markdown: Documentation-friendly format
Troubleshooting
Evaluation taking too long
Reduce article count (try 5 articles first)
Fewer models (2-3 instead of 5+)
Check API rate limits
No results appearing
Verify API keys are configured
Check article selection criteria (did you get 0 articles?)
Look at browser console for errors
Inconsistent results
AI models are non-deterministic (slight variation is normal)
Run multiple evaluations and average results
Use higher article counts for statistical significance
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