Sanne van den Bogaart - Explainable AI in the LIME-light

LIME (Local Interpretable Model-agnostic Explanations) is a framework that helps explain individual predictions from any machine learning model in an interpretable way

Key features of LIME:

• Model agnostic - works with any ML model
• Provides local explanations for specific predictions
• Supports tabular, text, and image data
• Faster than alternatives like SHAP
• Easy to use with a simple 3-step framework

Main reasons to use explainable AI:

• Legal requirements (EU AI Act)
• User/stakeholder requirements for transparency
• Model improvement and validation
• Building trust in model predictions

LIME workflow:

1. Initialize LIME explainer
2. Create explanation for single instance
3. Output visualization/interpretation

LIME creates explanations by:

• Generating perturbations around the instance
• Training a simple linear model locally
• Identifying feature importance for the prediction

Limitations and considerations:

• Only explains individual predictions, not global model behavior
• Need domain expertise to validate explanations
• Must ensure perturbations create valid data points
• Not built-in support for all data types

Best practices:

• Validate explanations with subject matter experts
• Check both normal and edge cases
• Use explanations to improve model performance
• Consider interaction effects between features