Computing system and method for producing and utilizing mechanistic explanations for deep-learning artificial intelligence models

A non-transitory computer-readable medium, wherein the non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing platform to:

• obtain a dataset that comprises at least one defined set of input questions for use in conducting a mechanistic interpretability investigation of a given deep-learning artificial intelligence (AI) model's performance of a given task;
• utilize the at least one defined set of input questions to carry out runs of the given deep-learning AI model;
• based on the runs of the given deep-learning AI model, conduct the mechanistic interpretability investigation of the given deep-learning AI model and thereby produce mechanistic explanations for the given deep-learning AI model's performance of the given task;
• utilize the mechanistic explanations for the given deep-learning AI model's performance of the given task as a basis for quantizing the given deep-learning AI model.

A method carried out by a computing platform, the method comprising:

• obtaining a dataset that comprises at least one defined set of input questions for use in conducting a mechanistic interpretability investigation of a given deep-learning artificial intelligence (AI) model's performance of a given task;
• utilizing the at least one defined set of input questions to carry out runs of the given deep-learning AI model;
• based on the runs of the given deep-learning AI model, conducting the mechanistic interpretability investigation of the given deep-learning AI model and thereby producing mechanistic explanations for the given deep-learning AI model's performance of the given task;
• utilizing the mechanistic explanations for the given deep-learning AI model's performance of the given task as a basis for quantizing the given deep-learning AI model.