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Nested Model for AI Design and Validation

Welcome to the Nested Model for AI Design and Validation repository! This project introduces a comprehensive framework designed to address the complexities of AI regulation and governance. Our model emphasizes trust, transparency, fairness, and the mitigation of discrimination issues in AI systems.

📊 Overview

The nested model consists of five interconnected layers, each addressing a specific aspect of AI design and validation:

  1. Regulation Layer

    • Ensures compliance with existing regulations and ethical standards.
    • Serves as the foundational layer for subsequent design and validation processes.

  2. Domain Layer

    • Focuses on the specific requirements and constraints of the domain where the AI will be applied.
    • Ensures relevance and effectiveness of the AI system in its intended context.

  3. Data Layer

    • Addresses the quality, representativeness, and fairness of the data used for training and validating models.
    • Aims to prevent biases and enhance model performance.

  4. Model Layer

    • Deals with the technical specifics of the AI model, including architecture, parameters, and interpretability.
    • Ensures robustness and reliability of the model.

  5. Prediction Layer

    • Focuses on the accuracy and reliability of model predictions, ensuring alignment with intended use cases.

🚀 Features

  • Interactive Nested Model: Explore the nested model interactively and understand its layers and functionalities.
  • Comprehensive Framework: A holistic approach that integrates technical, ethical, regulatory, and societal factors.
  • Recommendations for Practitioners:
    • Distinguishing contributions across layers.
    • Explicitly stating assumptions to provide context.
    • Promoting rigorous testing and validation for compliance and reliability.

📚 Citation

Dubey, Akshat, Zewen Yang, and Georges Hattab. "A Nested Model for AI Design and Validation." Cell Press iScience (2024). DOI: 10.1016/j.isci.2024.110603