Methods for explainability of deep-learning models

A system comprising:

• one or more processors;
• memory storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• feeding a first set of input features to an artificial intelligence (AI) model;
• obtaining a first set of raw output predictions from the AI model;
• determining a first set of impact scores for one or more of the input features fed into the AI model;
• training a neural network with the first set of impact scores as input to the network and pre-determined sentences describing the model's behavior as output;
• feeding a second set of input features to the AI model;
• obtaining a second set of raw output predictions from the model;
• determining a second set of impact scores based on the second set of output predictions;
• feeding the second set of impact scores to the neural network;
• and
• generating a sentence describing the AI model's behavior on the second set of input features.