System and method for identifying when to retrain an artificial intelligence model

A system for identifying when to retrain an artificial intelligence (AI) model, 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 an AI model and a set of target data samples on a predefined time period for identifying when to retrain the AI model;
• implement an artificial intelligence technique to generate a plurality of counterfactuals and corresponding target data samples among the set of target data samples for analyzing performance of the received AI model;
• compute an average counterfactual distance between closest counterfactuals and corresponding target data samples on the predefined time period;
• compare the average counterfactual distance to a predefined threshold value;
• identify that the AI model needs to be retrained when output data from comparing indicates that the average counterfactual distance is less than the predefined threshold value;
• and
• automatically retrain the AI model when it is determined that the average counterfactual distance is less than the predefined threshold value.

A non-transitory computer readable medium configured to store instructions for identifying when to retrain an artificial intelligence (AI) model, the instructions, when executed, cause a processor to perform the following:

• receiving an AI model and a set of target data samples on a predefined time period for identifying when to retrain the AI model;
• implementing an artificial intelligence technique to generate a plurality of counterfactuals and corresponding target data samples among the set of target data samples for analyzing performance of the received AI model;
• computing an average counterfactual distance between closest counterfactuals and corresponding target data samples on the predefined time period;
• comparing the average counterfactual distance to a predefined threshold value;
• identifying that the AI model needs to be retrained when output data from comparing indicates that the average counterfactual distance is less than the predefined threshold value;
• and
• automatically retraining the AI model when it is determined that the average counterfactual distance is less than the predefined threshold value.