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

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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

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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

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Quick Start

Step 1: Create New Evaluation

1. Click New Evaluation button

2. Enter evaluation name (e.g., "AI News Bias Test - January 2025")

3. Add optional description

4. 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

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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

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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

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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