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

Claim 26:
27. A method of training a fair prediction model, the method comprising:
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;
executing, by the computing device, the trained prediction model to define a predicted target variable value for each observation vector of the plurality of observation vectors;
(A) computing, by the computing device, 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) relabeling, by the computing device, 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) retraining, by the computing device, 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) executing, by the computing device, 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) computing, by the computing device, 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) computing, by the computing device, a constraint violation matrix from the computed conditional moments matrix;
(G) repeating, by the computing device, (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
outputting, by the computing device, the retrained prediction model.