System and method for generating recourse paths with privacy guarantees

A system for generating realistic multi-step recourse paths with privacy guarantees, 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 that is usable for training a machine learning model;
• train the machine learning model by using the at least the first set training data;
• implement a data distribution sampling algorithm on the first set of training data to generate corresponding sampled data by partitioning the first set of training data into non-overlapping subsets with differentially private clustering;
• compute a plurality of cluster centers with differential privacy guarantees for each of said plurality of cluster centers;
• generate a graph that connects each cluster center with different weights between each cluster center;
• and
• automatically generate, for a data point that receives a negative outcome from the trained model, a recourse path with privacy guarantees based on a plurality of set points from the graph that provides shortest path to output a positive outcome.

A non-transitory computer readable medium configured to store instructions for generating realistic multi-step recourse paths with privacy guarantees, the instructions, when executed, cause a processor to perform the following:

• receiving a first set of training data that is usable for training a machine learning model;
• training the machine learning model by using the at least the first set training data;
• implementing a data distribution sampling algorithm on the first set of training data to generate corresponding sampled data by partitioning the first set of training data into non-overlapping subsets with differentially private clustering;
• computing a plurality of cluster centers with differential privacy guarantees for each of said plurality of cluster centers;
• generating a graph that connects each cluster center with different weights between each cluster center;
• and
• automatically generating, for a data point that receives a negative outcome from the trained model, a recourse path with privacy guarantees based on a plurality of set points from the graph that provides shortest path to output a positive outcome.