Machine Learning - Applications

The Importance of XAI

Business and AI
How can I trust your model?
How does your model really make its decisions?

Key Point: Model performance is not the run-time or execution performance.

You might need to sacrifice model performance since they always do not yield the best results due to inherent problems of high bias (linear models) or high variance, leading to overfitting.

Understanding Machine Learning Model Interpretation

Fairness: What drives model predictions?
Accountability: Why did the model take a certain decision?
Transparency: How can we trust model predictions?

A famous example: White Guy Problem

Traditional Techniques for Model Interpretation

Exploratory analysis and visualization techniques:
Clustering and Dimension Reduction .etc

Model performance evaluation metrics:
Precision, Recall .etc

TSNE Visualization

Other Visualization

Limitations of Traditional Techniques

Good at : Data, Features, model evaluation
Limit on : Human-interpretable ways of how a model works
Model’s performance often decreases due to variability in data features.

Business Views

Just telling the business, “I have a model with 90% accuracy” is not sufficient information for them to start trusting the model when deployed in the real world.

We need human-interpretable interpretations (HII) of a model’s decision policies which could be explained with proper and intuitive inputs and outputs.

This would enable insightful information to be easily shared with peers (analysts, managers, data scientists, data engineers).

The Accuracy vs. Interpretability Trade-off

Classic Model Accuracy VS Model Interpretability Trade-off