Pairwise Shapley Values
(New advancement over SHAP for more human-intuitive explanations)
Idea in simple terms
Instead of explaining a prediction by looking only at features, you explain it by comparing the instance to another similar instance and showing which features caused the difference.
Why it matters
SHAP is powerful but can be noisy, expensive, and sometimes unintuitive.
Pairwise SHAP makes explanations more local, human-aligned, and comparative.
How it works
Choose a reference instance (e.g., another customer with a different credit score).
Compute a Shapley value for each feature, measuring how much that feature contributed to the difference in predictions between the two instances.
Aggregate contributions across pairs for a global view.
Best use cases
Credit decisions
Risk scoring
Any domain where explanations must compare similar cases
