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20/08/2026 5:43 pm
TL;DR: Different explanation methods answer different governance questions in AI systems, so recourse and feature contribution should not be treated as interchangeable according to Fiddler. Governance falls short when baselines, feasibility, or feature interactions are not checked against the decision workflow.
Editorial analysis by NHI Mgmt Group, based on content published by Fiddler: “Understanding Counterfactual and Attribution Explanations in AI”.
Key questions
Q: Why do counterfactual explanations and attribution methods often tell different stories?
A: They answer different questions.
Q: How do teams know whether an explanation is actually useful in production?
A: An explanation is useful when it survives baseline checks, respects feasibility constraints, and matches the operational purpose for which it is used.
Q: What breaks when explainability methods are treated as proof of model trustworthiness?
A: They break because explainability only describes one slice of the decision process.
Practitioner guidance
- Separate recourse from contribution analysis Use counterfactual explanations only when the business question is what input change could alter a decision, and use attribution methods when the question is which inputs drove the current output.
- Stress test baseline assumptions Run explanation checks against multiple baselines and compare how SHAP, LIME, or Integrated Gradients shift when the reference point changes.
- Validate feasibility before publishing recourse Reject counterfactual suggestions that depend on immutable, causally linked, or operationally unrealistic feature changes such as income or tenure shifts that cannot be cleanly isolated.
Bottom line: Counterfactual and attribution explanations answer different governance questions, so using one as a substitute for the other weakens AI oversight.
Glossary terms: Attribution, Counterfactual Analysis, Baseline Sensitivity, Recourse
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This topic was modified 2 days ago by NHI Mgmt Group
20/08/2026 6:26 pm
Explainability is a governance control only when it is scoped to the question being asked. Counterfactual explanations and attribution explanations are often discussed together, but they answer different oversight needs. One supports recourse, the other supports feature contribution analysis, and neither should be mistaken for model assurance on its own. For AI governance, that distinction matters because a readable explanation can still be wrong, incomplete, or operationally infeasible. Practitioners should treat explanation type as part of control design, not as a generic transparency label.
A question worth separating out:
Q: When should AI governance teams prefer recourse explanations over attribution explanations?
A: Prefer recourse explanations when the goal is to help a person or workflow understand what change could produce a different decision. Prefer attribution explanations when the goal is to inspect feature influence on the current result. Many programmes need both, but for different governance decisions.
👉 Read our full editorial: Counterfactual and attribution methods need different governance checks
This post was modified 2 days ago by NHI Mgmt Group