Algorithmic-Fairness-Dashboard

Algorithmic Fairness Dashboard ⚖️

📌 Overview

Algorithmic fairness is a critical aspect of machine learning, ensuring that models do not disproportionately favor or disadvantage specific demographic groups. The Algorithmic Fairness Dashboard is an interactive visualization tool designed to analyze and audit the fairness of machine learning models using various fairness metrics.

🎯 Project Goals

• Evaluate fairness across different demographic groups.
• Visualize fairness metrics using an interactive dashboard.
• Provide actionable insights to mitigate bias in models.
• Support various fairness definitions (e.g., demographic parity, equalized odds).

📂 Project Structure

algorithmic_fairness_dashboard/
│── data/                     # Sample datasets for testing fairness
│── notebooks/                # Jupyter notebooks for exploratory analysis
│── src/                      # Source code for fairness analysis
│   │── fairness_metrics.py   # Functions to compute fairness metrics
│   │── dashboard.py          # Code to render the interactive dashboard
│── tests/                    # Unit tests for fairness evaluation
│── README.md                 # Project documentation
│── requirements.txt          # Dependencies
│── setup.py                  # Installation script

🚀 Installation

Clone the repository and install dependencies:

git clone https://github.com/yourusername/algorithmic_fairness_dashboard.git
cd algorithmic_fairness_dashboard
pip install -r requirements.txt

🛠️ Usage

1. Prepare Your Data: Ensure your dataset includes sensitive attributes (e.g., gender, race, age).
2. Run Fairness Analysis:

   python src/fairness_metrics.py --input data/model_predictions.csv

3. Launch Dashboard:

   python src/dashboard.py

4. Interpret Results: Use the interactive dashboard to explore fairness metrics and identify potential biases.

📊 Fairness Metrics Included

• Demographic Parity: Measures whether the prediction rates are equal across groups.
• Equalized Odds: Compares false positive and false negative rates across groups.
• Disparate Impact: Evaluates the ratio of favorable outcomes between groups.
• Statistical Parity Difference: Quantifies the difference in positive prediction rates.

🏗️ Future Work

• Add support for more fairness definitions.
• Integrate model debugging features.
• Allow real-time monitoring of fairness during training.

📜 License

This project is licensed under the MIT License.

🤝 Contributing

We welcome contributions! Feel free to open an issue or submit a pull request.