AI-Ethica - Ethical AI Framework

Build AI that is fair, transparent & accountable in 5 lines of code.

AI-Ethica provides production-ready tools for evaluating fairness, detecting bias, ensuring transparency, and maintaining accountability in your AI systems.

Try the 2-min Tutorial View on GitHub

About

AI-Ethica provides tools, metrics, and guidelines for assessing fairness, bias, transparency, accountability, and privacy in artificial intelligence systems. Our framework helps developers and researchers build more ethical and responsible AI systems.

View on GitHub Documentation

Features

🔍 Bias Detection

Identify and measure bias in datasets and models. Detect representation bias, label bias, and measurement bias across protected attributes.

⚖️ Fairness Metrics

Calculate various fairness metrics including demographic parity, equalized odds, equal opportunity, and calibration across protected groups.

🔎 Transparency Analysis

Assess model interpretability, explainability, and transparency. Evaluate feature importance and documentation completeness.

🔒 Privacy Evaluation

Evaluate data privacy and security measures. Assess re-identification risk, data minimization, and privacy protection mechanisms.

📋 Accountability Tracking

Track model decisions and maintain audit trails. Log incidents and generate accountability reports for compliance and review.

📚 Comprehensive Documentation

Well-documented API with examples, best practices, and guidelines for ethical AI development.

Quick Start

Installation

pip install -r requirements.txt

Basic Usage

from ai_ethica import BiasDetector, FairnessMetrics, TransparencyAnalyzer

# Detect bias in a dataset
detector = BiasDetector()
bias_report = detector.analyze(
    dataset, 
    protected_attributes=['gender', 'race']
)

# Calculate fairness metrics
metrics = FairnessMetrics()
fairness_scores = metrics.evaluate(
    model, 
    test_data, 
    protected_attributes
)

# Assess model transparency
analyzer = TransparencyAnalyzer()
transparency_score = analyzer.assess(model)

API Reference

View the complete source code or explore the examples.

Core Modules

Bias Detection

The BiasDetector class helps identify statistical disparities, representation bias, and label bias in your datasets and models.

Fairness Metrics

The FairnessMetrics class provides implementations of common fairness definitions including demographic parity, equalized odds, and equal opportunity.

Transparency Analysis

The TransparencyAnalyzer evaluates model interpretability and provides recommendations for improving transparency.

Privacy Evaluation

The PrivacyEvaluator assesses privacy risks and evaluates data protection measures in your AI systems.

Accountability Tracking

The AccountabilityTracker maintains audit trails of model decisions and incidents for compliance and review.

Project Structure

AI-Ethica/
├── ai_ethica/           # Main package
│   ├── bias/           # Bias detection modules
│   ├── fairness/       # Fairness metrics
│   ├── transparency/   # Transparency tools
│   ├── privacy/        # Privacy evaluation
│   └── accountability/ # Accountability framework
├── examples/           # Example usage
├── tests/              # Unit tests
└── docs/               # Documentation

Contributing

We welcome contributions! Please see our Contributing Guidelines for more information.

Areas where we'd love contributions:

New fairness metrics
Additional bias detection methods
Privacy evaluation techniques
Documentation improvements
Example notebooks

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.