CPC G06N 5/04 (2013.01) [G06N 20/00 (2019.01)] | 17 Claims |
1. A post-processing computer-implemented method for post-hoc improvement of instance-level and group-level prediction metrics, the post-processing method comprising:
training a bias detector on a payload data that learns to detect a sample in a customer model that has an individual bias greater than a predetermined individual bias threshold value with constraints on a group bias, the sample being a member of an unprivileged group, wherein, during the training:
the bias detector perturbs a protected attribute in the payload data for the unprivileged group and computes the individual bias as an individual bias score by finding a difference between a probability of a favorable outcome for the perturbed protected attribute to original data of the payload data;
flagging the unprivileged group samples that have the individual bias greater than the predetermined individual bias threshold value; and
training the bias detector to discriminate between the flagged samples and un-flagged samples;
applying, in a run-time, the bias detector on a run-time sample to select a biased sample in the run-time sample having an individual bias greater than the predetermined individual bias threshold value; and
suggesting, in the run-time, a de-biased prediction for the biased sample by perturbing the protected attribute and checking for bias after perturbation.
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