System and method for generating recourse with data-driven actionability constraints

A system for generating recourse data with data-driven actionability constraints for a negatively classified individual, the system comprising:

• a processor;
• and
• a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to:
• receive a first set of training data and a second set of training data that are usable for training a machine learning model, each training data including a plurality of features associated with a positive decision or a negative decision on applications received from individuals seeking a pre-desired service from an institution;
• train the machine learning model by using the first set of training data and the second set of training data to output risk classification data associated with the negative decision;
• identify, based on the risk classification data, a negatively classified individual who received the negative decision;
• apply, for the negatively classified individual, a feature attribution algorithm to the trained machine learning model;
• rank, in response to applying the feature attribution algorithm, a list of features that explain a negative classification for the negatively classified individual;
• compute, by implementing a preconfigured algorithm, actionability labels for features that are same as the list of features that explain the negative classification for the negatively classified individual from a sampled time series historical data collected by identifying different individuals who have previously successfully improved their performance on negatively classified features over a predefined period of time to change a previously received negative decision to a positive decision;
• filter the list of features that explain the negative classification for each negatively classified individual by utilizing the computed actionability labels for all features;
• identify a list of features as significant and actionable that are likely to improve over the predefined period of time based on the filtered list of features;
• generate a recourse data for the negatively classified individual based on the identified list of features as significant and actionable that are likely to improve over the predefined period of time to change the received negative decision to a positive decision.

A non-transitory computer readable medium configured to store instructions for generating recourse data with data-driven actionability constraints for a negatively classified individual, the instructions, when executed, cause a processor to perform the following:

• receiving a first set of training data and a second set of training data that are usable for training a machine learning model, each training data including a plurality of features associated with a positive decision or a negative decision on applications received from individuals seeking a pre-desired service from an institution;
• training the machine learning model by using the first set of training data and the second set of training data to output risk classification data associated with the negative decision;
• identifying, based on the risk classification data, a negatively classified individual who received the negative decision;
• applying, for the negatively classified individual, a feature attribution algorithm to the trained machine learning model;
• ranking, in response to applying the feature attribution algorithm, a list of features that explain a negative classification for the negatively classified individual;
• computing, by implementing a preconfigured algorithm, actionability labels for features that are same as the list of features that explain the negative classification for the negatively classified individual from a sampled time series historical data collected by identifying different individuals who have previously successfully improved their performance on negatively classified features over a predefined period of time to change a previously received negative decision to a positive decision;
• filtering the list of features that explain the negative classification for each negatively classified individual by utilizing the computed actionability labels for all features;
• identifying a list of features as significant and actionable that are likely to improve over the predefined period of time based on the filtered list of features;
• generating a recourse data for the negatively classified individual based on the identified list of features as significant and actionable that are likely to improve over the predefined period of time to change the received negative decision to a positive decision.