  Evaluating the performance of an algorithm is crucial. Evaluating the
performance of data imputation and data augmentation can be similar since both
generated data can be compared with an original distribution. Although, the
typical evaluation metrics have the same flaw: They calculate the feature's
error and the global error on the generated data without weighting the error
with the feature importance. The result can be good if all of the feature's
importance is similar. However, in most cases, the importance of the features
is imbalanced, and it can induce an important bias on the features and global
errors. This paper proposes a novel metric named "Explainable Global Error
Weighted on Feature Importance"(xGEWFI). This new metric is tested in a whole
preprocessing method that 1. detects the outliers and replaces them with a null
value. 2. imputes the data missing, and 3. augments the data. At the end of the
process, the xGEWFI error is calculated. The distribution error between the
original and generated data is calculated using a Kolmogorov-Smirnov test (KS
test) for each feature. Those results are multiplied by the importance of the
respective features, calculated using a Random Forest (RF) algorithm. The
metric result is expressed in an explainable format, aiming for an ethical AI.
