An active machine learning system, the system comprising: a labeling component; an auxiliary machine learning component configured to produce a first score to an unlabeled observation for use in the labeling component to create at least one new labeled observation; a target machine learning component configured to produce a second score to the unlabeled observation, wherein the target machine learning component and the auxiliary machine learning component are from different machine learning component classes, and wherein the target machine learning component is a limited-capacity machine learning component that is limited in size for memory-efficient deployment; a comparison component configured to compare a magnitude in a difference between the first score and the second score such that the comparison component 118 can compare the first score from the auxiliary machine learning component and the second score from the target machine learning component to determine a probability that a false positive or false negative result has been produced by the target machine learning model to thereby determine a scope of the color-blindness of the target machine learning component; a featuring component configured to identify a machine learning feature using the magnitude; a refining component configured to update the target machine learning component, by incrementally adding or removing at least one feature to or from the target machine learning component such that the scope of color-blindness is reduced; and wherein the labeling component is configured to retrain the auxiliary machine learning component with the machine learning feature subsequent to the adding or removing at least one feature to or from the target machine learning component by the refining component, wherein the retrained version of the auxiliary machine learning component produces the at least one new labeled observation using the machine learning feature subsequent to adding or removing at least one feature to or from the target machine learning component. a labeling component; an auxiliary machine learning component configured to produce a first score to an unlabeled observation for use in the labeling component to create at least one new labeled observation; a target machine learning component configured to produce a second score to the unlabeled observation, wherein the target machine learning component and the auxiliary machine learning component are from different machine learning component classes, and wherein the target machine learning component is a limited-capacity machine learning component that is limited in size for memory-efficient deployment; a comparison component configured to compare a magnitude in a difference between the first score and the second score such that the comparison component 118 can compare the first score from the auxiliary machine learning component and the second score from the target machine learning component to determine a probability that a false positive or false negative result has been produced by the target machine learning model to thereby determine a scope of the color-blindness of the target machine learning component; a featuring component configured to identify a machine learning feature using the magnitude; a refining component configured to update the target machine learning component, by incrementally adding or removing at least one feature to or from the target machine learning component such that the scope of color-blindness is reduced; and wherein the labeling component is configured to retrain the auxiliary machine learning component with the machine learning feature subsequent to the adding or removing at least one feature to or from the target machine learning component by the refining component, wherein the retrained version of the auxiliary machine learning component produces the at least one new labeled observation using the machine learning feature subsequent to adding or removing at least one feature to or from the target machine learning component.