Stability and Robustness
Two nearly identical loan applications should not receive wildly different explanations unless the model itself crosses a meaningful boundary.
Stability measures explanation consistency under small input or sampling changes. Robustness asks whether the method resists intentional manipulation.
Local explanation sensitivity
A simple stability score compares explanation vectors:
The exact metric depends on the explanation type, but the principle is consistent: small benign changes should not create arbitrary stories.
Compare Neighboring Explanations
Add a benign input perturbation and rerun the stochastic explainer with a new seed. Compare coefficient rank, sign, and distance.
Explanation distance
0.284
Original
Neighbor
Run stochastic methods with multiple seeds. Test paraphrases for text, small transformations for images, and plausible neighboring records for tabular data.
Also test adversarial cases. Explanation methods can be manipulated to hide bias while preserving model predictions.
When is explanation instability expected rather than defective?