Providing explainable artificial intelligence using distributed ledger technology

A method comprising:

• at a computing device comprising at least one processor, a communication interface, and memory:
• training a first iteration of a deep learning model based on a training data set;
• storing, to a distributed ledger, a record of the first iteration of the deep learning model, wherein the record comprises:
• first clustering information, wherein the first clustering information identifies clusters of information of the first iteration of the deep learning model;
• and
• first rationale information, wherein the first rationale information identifies digital logic used to generate the clusters of information;
• storing records of additional iterations of the deep learning model;
• identifying, based on one or more outputs of the deep learning model, an anomalous output;
• identifying, by parsing the distributed ledger and based on identifying the anomalous output, one or more source iterations of the deep learning model, wherein the one or more source iterations correspond to the anomalous output;
• and
• initiating, based on identifying the one or more source iterations, one or more response actions configured to prevent additional anomalous outputs.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• train a first iteration of a deep learning model based on a training data set;
• store, to a distributed ledger, a record of the first iteration of the deep learning model, wherein the record comprises:
• first clustering information, wherein the first clustering information identifies clusters of information of the first iteration of the deep learning model;
• and
• first rationale information, wherein the first rationale information identifies digital logic used to generate the clusters of information;
• store records of additional iterations of the deep learning model;
• identify, based on one or more outputs of the deep learning model, an anomalous output;
• identify, by parsing the distributed ledger and based on identifying the anomalous output, one or more source iterations of the deep learning model, wherein the one or more source iterations correspond to the anomalous output;
• and
• initiate, based on identifying the one or more source iterations, one or more response actions configured to prevent additional anomalous outputs.