Cutoff value optimization for bias mitigating machine learning training system with multi-class target

A computing device comprising:

• a processor;
• and
• a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the computing device to
• (A) train a prediction model with a plurality of observation vectors, wherein each observation vector of the plurality of observation vectors includes a target variable value of a target variable, a sensitive attribute variable value of a sensitive attribute variable, and an attribute variable value for each attribute variable of a plurality of attribute variables, wherein the target variable has at least three possible unique values, wherein a predefined target event value indicates one of the at least three possible unique values of the target variable;
• (B) execute the trained prediction model to define a predicted target variable value and a probability associated with an accuracy of the defined predicted target variable value for each observation vector of the plurality of observation vectors;
• (C) compute a conditional moments matrix based on fairness constraints defined based on a fairness measure type, the predicted target variable value, and the sensitive attribute variable value of each respective observation vector of the plurality of observation vectors, wherein the predicted target variable value is identified as having the predefined target event value only when the probability associated with the accuracy is greater than a predefined event cutoff value;
• (D) repeat (A) through (C) to train a fair prediction model to reduce a bias value in predicting the target variable value based on the computed conditional moments matrix until a first stop criterion indicates retraining of the fair prediction model is complete;
• (E) compute an updated value for the predefined event cutoff value;
• (F) repeat (A) through (E) with the updated value for the predefined event cutoff value as the predefined event cutoff value until a second stop criterion indicates updating the predefined event cutoff value is complete;
• define an optimal event cutoff value from the predefined event cutoff values used when repeating (A) through (E);
• and
• output the defined optimal event cutoff value and the trained fair prediction model trained using the defined optimal event cutoff value.

A method of training a fair prediction model while defining an optimal event cutoff value, the method comprising:

• (A) training, by a computing device, a prediction model with a plurality of observation vectors, wherein each observation vector of the plurality of observation vectors includes a target variable value of a target variable, a sensitive attribute variable value of a sensitive attribute variable, and an attribute variable value for each attribute variable of a plurality of attribute variables, wherein the target variable has at least three possible unique values, wherein a predefined target event value indicates one of the at least three possible unique values of the target variable;
• (B) executing, by the computing device, the trained prediction model to define a predicted target variable value and a probability associated with an accuracy of the defined predicted target variable value for each observation vector of the plurality of observation vectors;
• (C) computing, by the computing device, a conditional moments matrix based on fairness constraints defined based on a fairness measure type, the predicted target variable value, and the sensitive attribute variable value of each respective observation vector of the plurality of observation vectors, wherein the predicted target variable value is identified as having the predefined target event value only when the probability associated with the accuracy is greater than a predefined event cutoff value;
• (D) repeating, by the computing device, (A) through (C) to train a fair prediction model to reduce a bias value in predicting the target variable value based on the computed conditional moments matrix until a first stop criterion indicates retraining of the fair prediction model is complete;
• (E) computing, by the computing device, an updated value for the predefined event cutoff value;
• (F) repeating, by the computing device, (A) through (E) with the updated value for the predefined event cutoff value as the predefined event cutoff value until a second stop criterion indicates updating the predefined event cutoff value is complete;
• defining, by the computing device, an optimal event cutoff value from the predefined event cutoff values used when repeating (A) through (E);
• and
• outputting, by the computing device, the defined optimal event cutoff value and the trained fair prediction model trained using the defined optimal event cutoff value.