XAI: Explainability of FND Models

Author: Manai Mortadha

Overview

This project focuses on eXplainable Artificial Intelligence (XAI) techniques applied to Financial Neural Decision (FND) models. The primary objective is to enhance transparency and interpretability in the decision-making process of FND models, providing insights into how these models arrive at specific conclusions or predictions in financial scenarios.

Key Features

• Implementation of various XAI techniques.
• Analysis of interpretability methods for FND models.
• Visual representation of feature importance and model explanations.

Project Structure

The repository is structured as follows:

• data/: Contains datasets and data processing scripts.
• models/: Includes the FND models and XAI implementation scripts.
• notebooks/: Jupyter notebooks illustrating XAI techniques applied to FND models.
• results/: Stores generated output, visualizations, and evaluation metrics.
• utils/: Utility functions and helper scripts.

Installation

To run this project locally, follow these steps:

1. Clone this repository:

   git clone https://github.com/manaimortadha/XAI-Explainability_of_FND_Models.git

2. Set up the required environment by installing dependencies:

   pip install -r requirements.txt

3. Run the notebooks or scripts in the respective folders.

Usage

The notebooks provided in the notebooks/ directory showcase the application of XAI techniques on FND models. Execute these notebooks to explore the explanations and interpretations generated.

Contributions

Contributions to this project are welcome. If you want to contribute, please follow these guidelines:

• Fork the repository.
• Create a new branch (git checkout -b feature/your-feature).
• Make your modifications.
• Commit your changes (git commit -am 'Add new feature').
• Push to the branch (git push origin feature/your-feature).
• Create a new Pull Request.

License

This project is licensed under the MIT License.