Building Explainable AI Models with Amazon SageMaker

Introduction

• The speaker is a data science student at a UK university and an AWS Cloud Club captain.
• The talk will focus on building explainable AI (XAI) models using Amazon SageMaker.

Why Do We Need Explainable AI?

• AI models often operate as "black boxes," making highly accurate predictions without explaining the reasoning behind them.
• Lack of transparency can lead to mistrust, especially in high-stakes decisions (e.g., hiring, self-driving cars).
• Understanding why AI systems make decisions is crucial for building trust and ensuring positive outcomes.

Explainable AI Methods

• There are two types of explainability: global and local.
• Global explainability shows how features affect predictions across the whole dataset, giving an overview of the model's behavior.
• Local explainability looks at how features impact specific predictions, providing insights into individual outcomes.
• Common methods include partial dependence plots, accumulated local effects, feature importances, individual conditional expectations, and SHAP (Shapley Additive Explanations).

Explainable AI with Amazon SageMaker

• Amazon SageMaker provides tools for building, training, and deploying machine learning models at scale.
• SageMaker Clarify can be used to add explainability components to the standard SageMaker workflow:
  • Pre-deployment explainability checks:
    • Detect model bias
    • Understand feature importance (SHAP values)
  • Post-deployment explainability:
    • Provide real-time explanations for model decisions
    • Ensure continued fairness and compliance

Examples

• Detecting model bias:
  • Conditional demographic disparity in labels shows that the disadvantaged group has a higher rejection rate.
• Explaining model decisions:
  • SHAP analysis shows that the "country" feature has the most impact on the model's decisions.

Benefits of Explainable AI in SageMaker

• Enhanced model transparency and trust
• Detection of model bias, promoting fairness
• Regulatory compliance
• Improved decision-making with actionable insights
• Simplified implementation for a wide range of users

Additional Resources

• Explainable AI specialization by the speaker's professor
• Fairness and explainability with SageMaker Clarify