Patent ID: 11922311
Assignee: SAS INSTITUTE INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
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;
execute the trained prediction model to define a predicted target variable value for each observation vector of the plurality of observation vectors;
(A) compute a weight value for each observation vector of the plurality of observation vectors based on the sensitive attribute variable value of each respective observation vector of the plurality of observation vectors, on fairness constraints defined based on a fairness measure type, and on whether the predicted target variable value of a respective observation vector of the plurality of observation vectors has a predefined target event value;
(B) relabel an observation vector of the plurality of observation vectors based on the computed weight value of each respective observation vector of the plurality of observation vectors;
(C) retrain the prediction model with each observation vector of the plurality of observation vectors weighted by a respective computed weight value and with the target variable value of any observation vector relabeled in (B);
(D) execute the prediction model retrained in (C) to define a second predicted target variable value for each observation vector of the plurality of observation vectors;
(E) compute a conditional moments matrix based on the fairness constraints and the second predicted target variable value and the sensitive attribute variable value of each respective observation vector of the plurality of observation vectors;
(F) compute a constraint violation matrix from the computed conditional moments matrix;
(G) repeat (A) through (F) until a stop criterion indicates retraining of the prediction model is complete, wherein the predicted target variable value in (A) is the second predicted target variable value; and
output the retrained prediction model.