feature importance
What It Means
Feature importance shows which pieces of input data your AI model relies on most heavily to make decisions. It's like understanding which factors a hiring manager weighs most when reviewing resumes - salary history, education, or experience. This measurement helps you see what your AI actually 'thinks' is most relevant, regardless of what you intended it to focus on.
Why Chief AI Officers Care
Feature importance is critical for regulatory compliance and risk management because it reveals whether your AI is making decisions based on protected characteristics like race or gender, even indirectly. It also helps identify when models are relying on unreliable or biased data sources, which could lead to poor business outcomes or legal liability. Understanding feature importance allows you to explain AI decisions to regulators, customers, and internal stakeholders.
Real-World Example
A bank's loan approval AI shows that ZIP code has the highest feature importance in determining creditworthiness, even higher than credit score or income. This reveals the model may be indirectly discriminating based on race or socioeconomic status, since ZIP codes often correlate with demographics, potentially violating fair lending laws and requiring immediate model adjustment.
Common Confusion
People often confuse feature importance with causation, thinking that high-importance features are necessarily the 'right' factors for the model to use. However, feature importance only shows correlation with outcomes - a model might heavily weight irrelevant or biased features that happen to correlate with the target variable in the training data.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, feature importance reveals which patient data elements (lab values, demographics, medical history) mos...
Finance: In financial AI models, feature importance reveals which variables most influence critical decisions like credit approva...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"how important the feature was for the classification performance of the model; a measure of the individual contribution of the corresponding feature for a particular classifier, regardless of the shape (e.g., linear or nonlinear relationship) or direction of the feature effect"Source: saarela_feature_2021
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