Counterfactual Explanations
Four valid counterfactuals for one denied applicant: earn $36,528 more, work 12 more years, hold minus 3.6 accounts, or turn 108.
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LIME
At 100 perturbations the top feature flipped between seeds. At a kernel width of 0.25 the surrogate’s R-squared was 0.0239 and it still ranked features.
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SHAP (SHapley Additive exPlanations)
Duplicate columns split credit equally in this symmetric example. Conditional and interventional SHAP can allocate dependent-feature credit differently.
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Post-hoc Explainability
In a synthetic example, a model never shown a protected attribute selected one group at 0.7823 and the other at 0.2502. The explanation named a postcode variable and was correct.
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Explainable Machine Learning
Three different questions wear the sentence “which features matter”.