Systems, methods, and graphical user interfaces for mitigating bias in a machine learning-based decisioning model

A computer-implemented method comprising:

• obtaining a decisioning dataset from a machine learning-based decisioning model, wherein the decisioning dataset includes a plurality of favorable decisioning records and at least one unfavorable decisioning record;
• converting, via a machine learning algorithm, the plurality of favorable decisioning records and the unfavorable decisioning record to a plurality of vector values in a multi-dimensional space;
• detecting, via the machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record, wherein the vector value of the favorable decisioning record and the vector value of the unfavorable decisioning record are a subset of the plurality of vector values;
• executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record, wherein the counterfactual assessment includes:
• retrieving attributes of the favorable decisioning record and the unfavorable decisioning record, and
• computing one or more bias intensity metrics based on disparities between the attributes of the favorable decisioning record and the unfavorable decisioning record;
• generating an explainability artifact that uses the one or more bias intensity metrics to explain a bias in the machine learning-based decisioning model, wherein explaining the bias in the machine learning-based decisioning model includes visually emphasizing that the machine learning-based decisioning model is generating unfair decisions;
• and
• in response to generating the explainability artifact, displaying the explainability artifact in a user interface that is accessible by a user, wherein the explainability artifact enables the user to execute a bias mitigation process that reconfigures one or more components of the machine learning-based decisioning model to mitigate the bias in the machine learning-based decisioning model.

A computer-implemented system comprising:

• one or more processors;
• a memory;
• and
• a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause a computing device to perform operations comprising:
• obtaining a decisioning dataset from a machine learning-based decisioning model, wherein the decisioning dataset includes a plurality of favorable decisioning records and at least one unfavorable decisioning record;
• converting, via a machine learning algorithm, the plurality of favorable decisioning records and the unfavorable decisioning record to a plurality of vector values in a multi-dimensional space;
• detecting, via the machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record, wherein the vector value of the favorable decisioning record and the vector value of the unfavorable decisioning record are a subset of the plurality of vector values;
• executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record, wherein the counterfactual assessment includes:
• retrieving attributes of the favorable decisioning record and the unfavorable decisioning record, and
• computing one or more bias intensity metrics based on disparities between the attributes of the favorable decisioning record and the unfavorable decisioning record;
• generating an explainability artifact that uses the one or more bias intensity metrics to explain a bias in the machine learning-based decisioning model, wherein explaining the bias in the machine learning-based decisioning model includes visually emphasizing that the machine learning-based decisioning model is generating unfair decisions;
• and
• in response to generating the explainability artifact, displaying the explainability artifact in a user interface that is accessible by a user, wherein the explainability artifact enables the user to execute a bias mitigation process that reconfigures one or more components of the machine learning-based decisioning model to mitigate the bias in the machine learning-based decisioning model.