Ethical Algorithm Tester

The ethical-algorithm-tester package provides tools for analyzing bias, fairness, transparency, and accountability in algorithmic decision-making. This package is useful for developers and data scientists who want to ensure that their algorithms operate ethically and fairly.

Features

• Bias Analysis: Evaluate the bias in algorithmic predictions based on specified attributes
• Fairness Analysis: Assess the fairness of decisions across different demographic groups
• Transparency Analysis: Explain the predictions made by your algorithms
• Accountability Analysis: Keep track of actions taken in the model development process

Installation

To install the package, run the following command:

npm install ethical-algorithm-tester

Usage

Here's an example of how to use the package:

const ethicalTester = require('ethical-algorithm-tester');

// Sample candidate data
const candidateData = [
    { age: '20-30', education: 'Bachelor', experience: 3, hireScore: 65 },
    { age: '30-40', education: 'Master', experience: 5, hireScore: 85 },
    { age: '40-50', education: 'PhD', experience: 8, hireScore: 90 },
    { age: '20-30', education: 'Master', experience: 2, hireScore: 70 },
    { age: '30-40', education: 'Bachelor', experience: 6, hireScore: 75 },
];

// Bias Analysis based on Age
const ageBias = ethicalTester.calculateBias(candidateData, 'age', 'hireScore');
console.log('Bias Analysis based on Age:', ageBias);

// Fairness Analysis based on Education Level
const educationFairness = ethicalTester.demographicParity(
    candidateData, 
    'education', 
    'hireScore'
);
console.log('Fairness Analysis based on Education Level:', educationFairness);

// Transparency Analysis
const hiringModel = {
    explain: (input) => `Explanation: Score based on ${JSON.stringify(input)}`,
};
const transparency = ethicalTester.explainPrediction(
    hiringModel, 
    { experience: 5, education: 'Master', age: '30-40' }
);
console.log('Transparency Explanation:', transparency);

// Accountability Analysis
const actionLogs = [
    { timestamp: '2024-10-10', action: 'data validation' },
    { timestamp: '2024-10-11', action: 'feature engineering' },
    { timestamp: '2024-10-12', action: 'model training' },
    { timestamp: '2024-10-13', action: 'bias check' },
];
const accountability = ethicalTester.accountabilityScore(actionLogs);
console.log('Accountability Score:', accountability);

API Reference

Bias Analysis

ethicalTester.calculateBias(data, attribute, scoreField)

Calculates bias in predictions based on specified attributes.

Fairness Analysis

ethicalTester.demographicParity(data, demographicField, scoreField)

Assesses fairness across different demographic groups.

Transparency Analysis

ethicalTester.explainPrediction(model, input)

Provides explanations for model predictions.

Accountability Analysis

ethicalTester.accountabilityScore(actionLogs)

Evaluates the accountability of the model development process.

Contributing

Contributions are welcome! We value any input, from fixing typos to suggesting new features or reporting bugs. How to Contribute

Fork the repository: https://github.com/emon273273/ethical-algorithm-tester Create your feature branch (git checkout -b feature/AmazingFeature) Commit your changes (git commit -m 'Add some AmazingFeature') Push to the branch (git push origin feature/AmazingFeature) Open a Pull Request

Guidelines

Ensure your code follows the existing style pattern Update the README.md with details of changes if applicable Update the documentation when adding new features Write meaningful commit messages

Issues

Feel free to submit issues and enhancement requests at https://github.com/emon273273/ethical-algorithm-tester/issues

For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

License

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

Support

If you have any questions or need help, please:

1. Check the documentation
2. Open an issue on GitHub
3. Contact the maintainers

Acknowledgments

• Thanks to all contributors who have helped make this package better
• Special thanks to the ethical AI community for guidance and best practices