Data privacy protection and removal for artificial intelligence model training and deployment

A system comprising:

• a non-transitory memory;
• and
• one or more hardware processors coupled to the non-transitory memory and configured to execute instructions to cause the system to:
• identify a content for a removal from training of a machine learning (ML) model that was previously trained using training data including the content;
• detect one or more usages of the content in at least one of the training data, an output by the ML model, or a source code file for the ML model;
• determine, based on the one or more usages, a concept learned by the ML model from the content in a vector space;
• identify a node activated during an execution of the ML model that is associated with the concept;
• and
• perform a selective parameter dampening of the node.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• testing at least one of training data previously used to train a machine learning (ML) model, an output by the ML model, or model weights of the ML model for content associated with one or more uses of data to be removed from training of the ML model or inferencing by the ML model, wherein the training data comprises the data to be removed;
• mapping the content to a concept learned by the ML model from the data in a vector space;
• determining a node in the vector space that is associated with the concept and is activated during an execution of the ML model;
• and
• perform a selective parameter dampening of the node for the execution of the ML model.