Explainable AI

Question 1

feature importance

Answer 1

• feature importance helps you understand why your models make their predictions
  
  • WHY?
    
    1. Explanations of model behavior are key for debugging auditing and understanding potential failure areas.
    2. help you to prioritize work to address data drift, because not all drift is created equal
    3. setup real-time alerting: text, emails,

Question 2

causal Inference

Answer 2

• most ANN determine correlations, not causation
• METHODS:
  
  • Double/Debiased ML: deconfound each input and then calculate causal inference of that feature
    
    • Train a model that ads a noise feature
    • Only works when you can identify confounding input

Question 3

feature redundancy

Answer 3

• DEFINITION: Feature redundancy is when you can use one to determine the other (e.g. they are correlated)
• the cons: Removing feature redundancy helps address the dependence of variables
• methods like SHAP work better on causal questions with strong feature independence

Question 4

unifold manifold approximation and projection (UMAP)

Answer 4

Unilateral manifold approximation and projection (UMAP) is visualization of embeddings used to map high dimensional data in a low dimensional space

• works for unstructured data if you break it down (e.g. certain types of patterns such as as color of hair