unbiased 0.0.0

Comprehensive bias detection and mitigation for datasets, models, and LLM agents

Unbiased

Comprehensive bias detection and mitigation for datasets, models, and LLM agents

Unbiased is a Python library that provides end-to-end fairness auditing across the entire ML pipeline - from raw data to deployed AI agents.

Features

🗂️ Dataset Audit

Detect statistical biases in tabular datasets before model training:

• Representation analysis (demographic imbalances)
• Label bias detection (outcome disparities)
• Proxy feature detection (hidden correlations)
• Missing data patterns
• Intersectional disparities
• KL divergence analysis

🤖 Model Audit

Audit trained ML models for fairness violations:

• Counterfactual flip testing
• 5 group fairness metrics (Demographic Parity, Disparate Impact, Equalized Odds, etc.)
• Intersectional bias analysis
• Automatic severity classification
• Mitigation recommendations

🧠 Agent Audit

Black-box bias auditing for LLM agents:

• System prompt auditing
• API endpoint testing
• Log replay analysis
• Persona-based counterfactual testing
• Statistical bias detection (CFR, MASD)
• Prompt surgery recommendations

Installation

# Basic installation (dataset and model audit)
pip install unbiased

# With LLM agent audit support
pip install unbiased[agent]

# With report generation (PDF, visualizations)
pip install unbiased[reports]

# Full installation with all features
pip install unbiased[all]

Quick Start

Dataset Audit

from unbiased import audit_dataset

report = audit_dataset(
    data='hiring_data.csv',
    protected_attributes=['gender', 'race'],
    target_column='hired',
    positive_value=1
)

print(f"Severity: {report.overall_severity}")
print(f"Findings: {len(report.findings)}")
report.export('dataset_audit.json')

Model Audit

from unbiased.model_audit import audit_model

report = audit_model(
    model='trained_model.pkl',
    test_data='test.csv',
    protected_attributes=['gender', 'race'],
    target_column='hired',
    positive_value=1
)

print(f"Severity: {report.overall_severity}")
for finding in report.findings:
    print(f"- {finding.title}: {finding.severity}")

Agent Audit

from unbiased.agent_audit import audit_agent

report = await audit_agent(
    system_prompt="You are a hiring assistant...",
    seed_case="Evaluate candidate: Name: Alex...",
    api_key="your-api-key"
)

print(f"Bias detected: {report.has_bias}")
print(f"CFR: {report.cfr:.3f}")

Documentation

• Dataset Audit Guide
• Model Audit Guide
• Agent Audit Implementation Guide
• API Reference

Requirements

• Python 3.8+
• pandas >= 2.0.0
• numpy >= 1.24.0
• scipy >= 1.10.0
• scikit-learn >= 1.3.0

Optional Dependencies

• Agent audit: groq, openai, anthropic, aiohttp
• Reports: reportlab, matplotlib
• Embeddings: sentence-transformers
• LangGraph: langgraph, langchain

Research Foundation

Unbiased implements methods from cutting-edge fairness research:

• CAFFE (Parziale et al. 2025) - Counterfactual fairness evaluation
• CFR/MASD (Mayilvaghanan et al. 2025) - Statistical bias metrics
• Hardt et al. (2016) - Equality of opportunity
• Chouldechova (2017) - Fair prediction with disparate impact
• EEOC Guidelines - 80% rule for disparate impact

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Citation

If you use Unbiased in your research, please cite:

@software{unbiased2025,
  title={Unbiased: Comprehensive Bias Detection and Mitigation Library},
  author={NoBias Team},
  year={2025},
  url={https://github.com/nobias/unbiased}
}

Support

• GitHub Issues: https://github.com/nobias/unbiased/issues
• Documentation: https://github.com/nobias/unbiased/blob/main/docs
• Email: [e-mail]