文本偏见检测服务

pip install claudette-bias-detector

npm install @claudette-mcp/bias-detector

git clone https://github.com/claudette-mcp/bias-detector.git
cd bias-detector
pip install -e .

{
  "mcpServers": {
    "bias-detector": {
      "command": "claudette-bias-detector",
      "args": ["--mode", "server"]
    }
  }
}

# Analyze text from command line
bias-detector analyze "The politician made controversial statements during the debate."

# Analyze from file
bias-detector analyze --file input.txt

# Get detailed report
bias-detector analyze --verbose "Sample text here"

# Specify bias types to check
bias-detector analyze --types political,gender "Sample text here"

from claudette_bias_detector import BiasDetector

# Initialize detector
detector = BiasDetector()

# Analyze text
result = detector.analyze(
    text="The politician made controversial statements during the debate.",
    bias_types=["political", "emotional", "confirmation"]
)

# Get results
print(f"Overall bias score: {result.overall_score}")
for bias in result.detected_biases:
    print(f"{bias.type}: {bias.confidence:.2%} - {bias.description}")

# Start API server
claudette-bias-detector --mode api --port 3000

# Make request
curl -X POST http://localhost:3000/api/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "text": "The politician made controversial statements.",
    "options": {
      "bias_types": ["political", "emotional"],
      "include_suggestions": true
    }
  }'

{
  "text": "The young CEO, despite her gender, successfully led the tech startup.",
  "analysis": {
    "overall_bias_score": 0.72,
    "detected_biases": [
      {
        "type": "gender_bias",
        "confidence": 0.85,
        "severity": "moderate",
        "indicators": [
          "despite her gender",
          "gender qualification"
        ],
        "description": "Language implies gender is a barrier to success",
        "suggestion": "Remove gender qualifiers: 'The young CEO successfully led the tech startup.'"
      },
      {
        "type": "age_bias",
        "confidence": 0.45,
        "severity": "low",
        "indicators": ["young CEO"],
        "description": "Age emphasis may suggest unexpected achievement",
        "suggestion": "Consider if age is relevant to the context"
      }
    ],
    "context_analysis": {
      "tone": "neutral-positive",
      "subject": "business/leadership",
      "potential_audience_bias": "professional"
    },
    "recommendations": [
      "Revise to remove gender-based qualifications",
      "Assess necessity of age-related descriptors",
      "Focus on achievements rather than demographic characteristics"
    ]
  },
  "metadata": {
    "analysis_time_ms": 87,
    "language": "en",
    "word_count": 12,
    "model_version": "1.0.0"
  }
}

{
  "default_bias_types": ["all"],
  "sensitivity": "medium",
  "language": "auto",
  "include_suggestions": true,
  "confidence_threshold": 0.5,
  "max_text_length": 50000,
  "output_format": "json",
  "custom_patterns": {
    "organizational_bias": {
      "keywords": ["our company", "our product"],
      "weight": 0.7
    }
  }
}

BIAS_DETECTOR_MODEL=default       # Model variant to use
BIAS_DETECTOR_LOG_LEVEL=info      # Logging level
BIAS_DETECTOR_CACHE_SIZE=1000     # Result cache size
BIAS_DETECTOR_API_KEY=xxx         # API authentication key (if using API mode)

class BiasDetector:
    def __init__(self, config: Optional[Config] = None)
    def analyze(self, text: str, bias_types: List[str] = None) -> BiasAnalysis
    def analyze_batch(self, texts: List[str]) -> List[BiasAnalysis]
    def compare(self, text1: str, text2: str) -> ComparisonResult
    def detect_patterns(self, texts: List[str]) -> PatternAnalysis

// Available MCP tools
tools: [
  {
    name: "analyze_bias",
    description: "Detect and analyze bias in text",
    parameters: { text: string, bias_types?: string[] }
  },
  {
    name: "compare_bias",
    description: "Compare bias between two texts",
    parameters: { text1: string, text2: string }
  },
  {
    name: "get_suggestions",
    description: "Get suggestions to reduce bias",
    parameters: { text: string, bias_type: string }
  }
]

Text Length    | Analysis Time | Memory Usage
---------------|---------------|-------------
100 words      | 45ms          | 12MB
1,000 words    | 87ms          | 24MB
10,000 words   | 340ms         | 68MB
50,000 words   | 1,420ms       | 185MB

# Run all tests
pytest

# Run with coverage
pytest --cov=bias_detector --cov-report=html

# Run specific test suite
pytest tests/test_political_bias.py

# Run performance tests
pytest tests/test_performance.py -v

{
  "mcpServers": {
    "bias-detector": {
      "command": "claudette-bias-detector",
      "args": []
    }
  }
}

from claudette_bias_detector import BiasDetector
import pandas as pd

detector = BiasDetector()

# Analyze DataFrame column
df['bias_score'] = df['text'].apply(lambda x: detector.analyze(x).overall_score)
df['biases_found'] = df['text'].apply(lambda x: len(detector.analyze(x).detected_biases))

# Visualize
df[['text', 'bias_score', 'biases_found']].head()

// Express.js integration
const axios = require('axios');

app.post('/check-bias', async (req, res) => {
  const { text } = req.body;

  const result = await axios.post('http://localhost:3000/api/analyze', {
    text,
    options: { bias_types: ['all'] }
  });

  res.json(result.data);
});