AI Glossary
SHAP (SHapley Additive exPlanations)
A method for explaining individual AI predictions by showing which factors contributed most to the output. SHAP increases transparency and helps build trust in AI decisions.
Understanding SHAP (SHapley Additive exPlanations)
SHAP answers the question "why did the AI make this decision?" by assigning each input feature a contribution score. If your AI denies a loan application, SHAP shows that income contributed -30%, debt-to-income ratio contributed -45%, and employment tenure contributed +15%.
For businesses, SHAP provides the explainability needed for regulatory compliance, internal auditing, and stakeholder trust. When AI decisions affect customers, employees, or financial outcomes, being able to explain the reasoning is not optional.
SHAP is especially valuable in regulated industries (banking, insurance, healthcare) where "the AI said so" is not an acceptable justification. It transforms AI from a black box into a transparent, auditable decision-making tool.
SHAP (SHapley Additive exPlanations) in Canada
Canadian financial institutions subject to OSFI guidelines are increasingly expected to explain AI-driven credit and risk decisions — SHAP is a key tool for meeting these explainability requirements.
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Frequently Asked Questions
Not necessarily. SHAP is most important for high-stakes decisions (lending, hiring, insurance) and regulated industries. For content generation or internal productivity tools, simpler explanations may suffice.
SHAP has well-maintained open-source libraries in Python that integrate with most ML frameworks. A data scientist can add SHAP explanations to existing models in a few days.
See SHAP (SHapley Additive exPlanations) in Action
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