Explainable AI

Overview

• Explainable AI (XAI) is an emerging field focused on making AI model behavior understandable.
• Aims to explain how AI models make predictions or results.

Key Concepts

• AI Black Box:
  • An AI system whose internal workings are not visible or understandable to users.
  • Can make accurate predictions but lacks transparency (e.g., classifying an image without explanation).
• Need for Explainability:
  • Engineers and scientists seek transparency in AI models.
  • XAI seeks to "unbox" these black boxes.

Explainability Techniques

• Trade-offs:
  • Explainability often comes at the expense of model power and accuracy.
• Model Types:
  • Deep Learning Models:
    • High predictive power but difficult to interpret.
  • Linear Regression/Decision Trees:
    • More interpretable and inherently explainable.
    • Decision trees allow tracing of prediction steps easily.

Techniques for Deep Learning Models

• Popular XAI techniques include:
  • Grad-CAM:
    • Uses gradients of classification scores with final convolutional feature map.
    • Highlights important parts of the image for predictions.
  • LIME (Local Interpretable Model-agnostic Explanations):
    • Approximates complex models with simpler ones (e.g., regression tree).
    • Determines feature importance as a proxy for the complex model.
  • Occlusion Sensitivity:
    • Computes changes in activation when parts of the input are occluded.
    • Moves occlusion mask across input data to assess classification score changes.

Importance of Explainability

• Helps build user confidence in AI decisions.
• Useful for comparing multiple models and selecting the best one based on right reasons.
• Example scenario:
  • If a model misclassifies important features, it indicates a potential malfunction.

Deployment Considerations

• AI models must transition from theory to practical applications.
• Before deploying:
  • Ensure trust in model decisions using XAI techniques.
  • Confirm the best-performing model is selected for production.
  • Ensure model functions as intended.

Relevance in Industries

• High importance in safety-critical or regulated industries.
• Explainability techniques can help identify and mitigate biases.

Additional Resources

• Check out our AI video playlist for more on XAI, machine learning, deep learning, and MATLAB applications.