System and method for mitigating bias in classification scores generated by machine learning models

A computing system comprising:

• at least one non-transitory computer-readable medium;
• one or more processors;
• and
• program instructions stored on the at least one non-transitory computer-readable medium that are executable by the one or more processors such that the computing system is configured to:
• train a machine learning model by carrying out a machine learning process on a training data set, wherein the trained machine learning model is configured to (i) receive an input vector comprising respective values for a given set of input variables and (ii) based on an evaluation of the received input vector, output a score for use in rendering a given type of classification decision;
• detect bias in the trained machine learning model relative to a protected class by (i) inputting a first set of input vectors associated with an unprotected class into the trained machine learning model and thereby producing a first set of scores associated with the unprotected class (ii) inputting a second set of input vectors associated with the protected class into the trained machine learning model and thereby producing a second set of scores associated with the protected class, (iii) performing a comparison between the first set of scores and the second set of scores, and (iv) based on the comparison, determining that the trained machine learning model exhibits a threshold level of bias;
• in response to determining that the trained machine learning model exhibits a threshold level of bias, identify one or more input variable groups that contribute to the bias by (i) dividing the trained machine learning model's given set of input variables into a plurality of input variable groups, wherein each respective input variable group includes a respective subset of the given set of input variables, (ii) determining respective bias contribution values for the plurality of input variable groups using a score explainer function, and (iii) based on the respective bias contribution values that are determined for the plurality of input variable groups, identifying the one or more input variable groups that contribute to the bias;
• mitigate bias in the trained machine learning model by producing a post-processed version of the trained machine learning model that comprises a respective transformation in place of each of the identified one or more input variable groups, wherein the post-processed version of the trained machine learning model is configured to (i) receive an input vector comprising respective values for a given set of input variables, (ii) for each of the identified one or more input variable groups, apply the respective transformation to the respective value for the input variable group, and (iii) based on an evaluation of the transformed input vector, output a score for use in rendering the given type of classification decision;
• and
• use the post-processed version of the trained machine learning model to render the given type of classification decision for a given input vector by (i) executing the post-processed version of the trained machine learning model, (ii) while executing the post-processed version of the trained machine learning model, inputting the given input vector into the post-processed version of the trained machine learning model and thereby causing the post-processed version of the trained machine learning model to output a given score for the given input vector, and (iii) comparing the given score to a classification threshold for the given type of classification decision.

A non-transitory computer-readable media storing computer instructions that, when executed by one or more processors, cause a computer system to perform functions comprising:

• training a machine learning model by carrying out a machine learning process on a training data set, wherein the trained machine learning model is configured to (i) receive an input vector comprising respective values for a given set of input variables and (ii) based on an evaluation of the received input vector, output a score for use in rendering a given type of classification decision;
• detecting bias in the trained machine learning model relative to a protected class by (i) inputting a first set of input vectors associated with an unprotected class into the trained machine learning model and thereby producing a first set of scores associated with the unprotected class (ii) inputting a second set of input vectors associated with the protected class into the trained machine learning model and thereby producing a second set of scores associated with the protected class, (iii) performing a comparison between the first set of scores and the second set of scores, and (iv) based on the comparison, determining that the trained machine learning model exhibits a threshold level of bias;
• in response to determining that the trained machine learning model exhibits a threshold level of bias, identifying one or more input variable groups that contribute to the bias by (i) dividing the trained machine learning model's given set of input variables into a plurality of input variable groups, wherein each respective input variable group includes a respective subset of the given set of input variables, (ii) determining respective bias contribution values for the plurality of input variable groups using a score explainer function, and (iii) based on the respective bias contribution values that are determined for the plurality of input variable groups, identifying the one or more input variable groups that contribute to the bias;
• mitigating bias in the trained machine learning model by producing a post-processed version of the trained machine learning model that comprises a respective transformation in place of each of the identified one or more input variable groups, wherein the post-processed version of the trained machine learning model is configured to (i) receive an input vector comprising respective values for a given set of input variables, (ii) for each of the identified one or more input variable groups, apply the respective transformation to the respective value for the input variable group, and (iii) based on an evaluation of the transformed input vector, output a score for use in rendering the given type of classification decision;
• and
• using the post-processed version of the trained machine learning model to render the given type of classification decision for a given input vector by (i) executing the post-processed version of the trained machine learning model, (ii) while executing the post-processed version of the trained machine learning model, inputting the given input vector into the post-processed version of the trained machine learning model and thereby causing the post-processed version of the trained machine learning model to output a given score for the given input vector, and (iii) comparing the given score to a classification threshold for the given type of classification decision.