Handling Missing Data
Techniques (e.g., imputation, deletion, modeling) for addressing gaps in datasets to maintain model integrity and fairness.
Missingness can bias models or reduce accuracy. Governance covers strategies: Deletion (remove incomplete records), Imputation (mean, median, model-based), or explicit Missing-Indicator features. Each choice must be documented, its impact on downstream fairness evaluated, and pipelines configured to handle missing values consistently in production.
A credit-risk dataset has 15% missing income values. The team compares mean-imputation, KNN-imputation, and a predictive-imputation model. They choose KNN-imputation (lowest RMSE), add a “was_income_missing” binary feature, and validate that the imputation does not skew approval rates for disadvantaged groups.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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