System and method for managing classification outcomes of data inputs classified into bias categories

A method, comprising:

• receiving, by a processor, a plurality of bias data categories;
• receiving, by the processor, a data input from a user for classification in a plurality of data categories;
• wherein the data input is inputted into both a classification machine learning model and a bias filter machine learning model;
• utilizing, by the processor, the classification machine learning model to classify the data input in at least one data category from the plurality of data categories and determine a first confidence probability in a classification outcome;
• utilizing, by the processor, the bias filter machine learning model to determine a second confidence probability that the classification outcome of classifying the data input into the at least one data category from the plurality of data categories is based on at least one bias characteristic associated with at least one bias data category from the plurality of bias data categories;
• utilizing, by the processor, a gate machine learning model to determine when to output the classification outcome of classifying the data input into the at least one data category from the plurality of data categories to a computing device of a user based at least in part on:
• i) the first confidence probability that is outputted by the classification machine learning model and inputted to the gate machine learning model,
• ii) the second confidence probability that is outputted by the bias filter machine learning model and inputted to the gate machine learning model, and
• iii) a predefined bias threshold;
• and
• outputting, by the processor, the classification outcome of classifying the data input into the at least one data category from the plurality of data categories to the computing device of the user based on the determination of the gate machine learning model.