Responsible AI - Azure Machine Learning

Introduction

Responsible AI empowers you to build and deploy machine learning models that are fair, explainable, and accountable. It provides a set of tools and practices to help you assess and mitigate potential risks associated with AI solutions.

Key Concepts

Understanding the fundamentals of Responsible AI is crucial for developing ethical and trustworthy AI solutions.

• Fairness: Ensuring models don't discriminate against protected groups.
• Explainability: Understanding how models make decisions.
• Accountability: Defining who is responsible for the AI system's behavior.
• Privacy: Protecting sensitive data used in the models.

Tools and Capabilities

Azure Machine Learning provides several tools to support Responsible AI development.

• Fairness Assessment: Evaluate model bias using various metrics.
• Explainable AI (XAI): Utilize techniques like SHAP and LIME for model explanations.
• Data Governance: Implement data quality checks and controls.

Getting Started

Start your journey with Responsible AI in Azure Machine Learning. Learn more about the Responsible AI overview. Perform fairness assessments.