Responsible AI Toolkit for Databricks (RAID)

Overview

Welcome to the Responsible AI Toolkit for Databricks, a growing collection of tools and resources to help organizations build, monitor, and maintain Responsible AI solutions on Databricks.

This repository is designed to support AI practitioners, data scientists, and compliance teams in meeting the stringent requirements of ethical AI deployment, including compliance with frameworks such as the new EU AI Act.

Initial Focus: High-Risk Machine Learning Auditing

The first set of tools included in this repository is tailored for auditing and reporting High-Risk Machine Learning systems, as defined by the EU AI Act. These tools help ensure compliance by enabling:

• Audit Trails: Comprehensive logs and records for High-Risk AI systems.
• Explainability Reporting: Documenting decision-making processes and ensuring transparency.
• Bias and Fairness Assessments: Identifying and mitigating unfair biases in ML models.
• Risk Assessment Templates: Prebuilt frameworks to classify and assess risks.

Reources

Databricks

These resources provide a comprehensive overview of Databricks' approach to responsible AI, covering aspects such as security, governance, regulatory compliance, and ethical considerations in AI development and deployment.

• [Amazing Responsible AI resources](resources/Databricks and Responsible AI.md)

General

• [Amazing, top regarded, Responsible AI resources] (resources/General.md)

Why This Toolkit?

With regulations like the EU AI Act imposing stringent requirements for auditing High-Risk AI systems, organizations need robust solutions to ensure compliance. This toolkit is specifically built for Databricks users, leveraging the platform's capabilities to streamline Responsible AI practices.

Features (Planned Additions)

Beyond High-Risk ML auditing, the toolkit aims to include:

1. Tools for ongoing AI compliance monitoring.
2. Frameworks for implementing ethical AI principles.
3. Integration examples for governance workflows on Databricks.
4. Best practices for deploying transparent and fair AI systems.

Getting Started

1. Clone the repository:

   git clone https://github.com/lherrera-db/RAID.git

2. Follow the Documentation for setup and usage instructions for each tool.

Contributing

Contributions are welcome! Please see the CONTRIBUTING.md for guidelines.

License

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