Insights into Explainable AI

Introduction

• Speaker: Akash, CEO & Lead Instructor at Jogin
• Focus: Understanding Explainable AI (XAI), its importance, types, and practical applications.

What is Explainable AI (XAI)?

• Definition: XAI refers to methods that make the outputs of AI models understandable to humans.
• Importance: Provides insights into why a model makes certain predictions.
• Use Cases: Insurance applications, ad clicks, health predictions (e.g., heart attack risk).

Why is Explainable AI Important?

1. Understanding Model Behavior:

   • Humans have an innate curiosity about how things work.
   • Stakeholders need to trust model outputs and understand the reasoning behind predictions.

2. Identifying Bias:

   • Datasets can contain biases that affect predictions (e.g., race, gender).
   • Understanding this bias is crucial for ethical AI.

3. Overcoming Data Leakage:

   • Hidden correlations in datasets can lead to misleading model performance.
   • Identifying and addressing these issues is crucial for reliable predictions.

4. Regulatory Compliance:

   • In sectors like healthcare and finance, regulations require explanations for decisions.

5. Improving Black Box Models:

   • Helps fine-tune models by identifying key features impacting performance.

Types of Explainable AI

1. Model-Based Explainability

• Definition: Techniques based on the structure of the model itself.
• Examples: Linear regression, logistic regression, decision trees (interpretable by design).

2. Post Hoc Explainability

• Definition: Techniques used after model training to explain predictions.
• Subtypes:
  • Black Box Approach: Analyzes inputs and outputs without using model internals.
  • White Box Approach: Analyzes specific parts of the model (e.g., layers in neural networks).

Classifications by Scope

• Global Explanations: Explain features’ importance across the entire dataset.
• Local Explanations: Focus on explaining individual predictions and the key features influencing them.

Techniques for Explainability

Case Studies on Heart Stroke Prediction

1. LIME (Local Interpretable Model-agnostic Explanations)

• Function: Fits a linear model to a small region around a specific prediction to derive feature importance.
• Visualization: Provides a local understanding of model prediction through simplified models.

2. SHAP (SHapley Additive exPlanations)

• Function: Uses game theory to quantify the contribution of each feature to a specific prediction.
• Process: Randomly removes features and measures the impact on predictions to derive feature importance.

3. Partial Dependence Plot (PDP)

• Function: Analyzes how the target variable changes as a specific feature varies across its range, while keeping other features constant.
• Key Assumption: Assumes independence between features for accurate interpretation.

Resources for Learning More

1. Interpretable ML Book: Discusses various interpretability methods and techniques.
2. Awesome Machine Learning Interpretability GitHub Repository: A comprehensive collection of resources and tutorials on XAI.
3. Deep Finder YouTube Channel: Offers tutorials on explainable AI with practical coding examples.
4. Kaggle Tutorial by Parul Pandey: Hands-on examples using datasets relevant to explainable AI.

Conclusion

• Query Handling: Addressed questions on XAI applications, model agnosticism, and beginner resources.
• Bootcamp: Promoted an upcoming data science bootcamp that covers various relevant topics, including explainable AI.
• Stay Updated: Encouraged joining a newsletter for the latest updates in AI research and techniques.