US 2026/0065129 A1Published application

Adaptive fairness repair pipeline for mitigating machine learning bias

AssigneeOptum Services (Ireland) Limited

InventorsDaniel KELLY, Karim Mahmoud Mohamed MOUSTAFA, Harutyun SHAHUMYAN, Arjit AGRAWAL

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Abstract

Various embodiments of the present disclosure provide improved bias mitigation techniques for machine learning technology. The bias mitigation techniques include receiving a machine learning biasing attribute and a set of outputs generated by a target machine learned model and determining a divergence score based at least in part on a difference between a first distribution and a second distribution of the set of outputs. In response to the divergence score meeting or exceeding a threshold, the bias mitigation techniques include generating, using a fairness learning model, a transformation to one or more set of outputs to decrease the divergence score and storing the transformation and an identifier. Through a plurality of iterations, a learned transformation sequence may be generated that may be applied to transform outputs of a machine learning model to mitigate machine learning bias.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving, by one or more processors, a machine learning biasing attribute and a set of outputs generated by a target machine learned model comprising a first subset of outputs generated using a first subset of input data associated with a first attribute value and a second subset of outputs generated using a second subset of input data associated with a second attribute value, wherein the first attribute value and the second attribute value are values of the machine learning biasing attribute;
determining, by the one or more processors, a divergence score based at least in part on a difference between a first distribution of the first subset of outputs and a second distribution of the second subset of outputs;
determining, by the one or more processors, that the divergence score meets or exceeds a divergence score threshold;
responsive to determining that the divergence score meets or exceeds the divergence score threshold, generating, by the one or more processors and using a fairness learning model, a transformation to one or more of the second subset of outputs or the second distribution of the second subset of outputs to decrease the divergence score;
storing, by the one or more processors, the transformation and an identifier of the first attribute value and the second attribute value;
and
generating, by the one or more processors, at least one of a transformed second subset of outputs or a transformed second distribution by altering at least one of the one or more of the second subset of outputs or the second distribution of the second subset of outputs using the transformation.

4 more elements

A system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:

receive a machine learning biasing attribute and a set of outputs generated by a target machine learned model comprising a first subset of outputs generated using a first subset of input data associated with a first attribute value and a second subset of outputs generated using a second subset of input data associated with a second attribute value;
determine a divergence score based at least in part on a first distribution of the first subset of outputs and a second distribution of the second subset of outputs;
determine that the divergence score meets or exceeds a divergence score threshold;
responsive to determining that the divergence score meets or exceeds the divergence score threshold, generate, using a fairness learning model, a transformation to one or more of the second subset of outputs or the second distribution of the second subset of outputs to decrease the divergence score;
store the transformation and an identifier of the first attribute value and the second attribute value;
and
generate at least one of a transformed second subset of outputs or a transformed second distribution by altering at least one of the one or more of the second subset of outputs or the second distribution of the second subset of outputs using the transformation.

4 more elements

One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:

receive a machine learning biasing attribute and a set of outputs generated by a target machine learned model comprising a first subset of outputs generated using a first subset of input data associated with a first attribute value and a second subset of outputs generated using a second subset of input data associated with a second attribute value;
determine a divergence score based at least in part on a first distribution of the first subset of outputs and a second distribution of the second subset of outputs;
determine that the divergence score meets or exceeds a divergence score threshold;
responsive to determining that the divergence score meets or exceeds the divergence score threshold, generate, using a fairness learning model, a transformation to one or more of the second subset of outputs or the second distribution of the second subset of outputs to decrease the divergence score;
store the transformation and an identifier of the first attribute value and the second attribute value;
and
generate at least one of a transformed second subset of outputs or a transformed second distribution by altering at least one of the one or more of the second subset of outputs or the second distribution of the second subset of outputs using the transformation.

4 more elements

Dependencies are inferred from the claim text.