Decoupling memory and computation to enable privacy across multiple knowledge bases of user data

A contextual trainer comprising:

• one or more processors;
• and
• memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, cause the contextual trainer to:
• receive, from a trained neural network, a latent activation representation h based on an input observation x received by the trained neural network;
• translate contextual data values from one or more knowledge bases into keys;
• generate gradients based on the keys;
• generate a Fair Region vector, the Fair Region vector comprising a subset of the gradients selected to provide a selectable distribution over the contextual data values;
• combine the latent activation representation h and the Fair Region vector;
• and
• output a predicted sequence ŷ based at least in part on the combined latent activation representation h and the Fair Region vector.

A method, for adapting a trained neural network response using customer contextual data while controlling bias, the method comprising:

• translating contextual data values from one or more knowledge bases into keys using a contextual trainer memory module;
• generating gradients based on the keys;
• generating a Fair Region vector, the Fair Region vector comprising a subset of the gradients selected to provide a predetermined distribution over the contextual data values;
• receiving, by a trained neural network, an input observation x;
• generating, by the trained neural network, a latent activation representation h based on the input observation x;
• querying the contextual trainer memory module with the latent activation representation h;
• combining the latent activation representation h and the Fair Region vector;
• and
• outputting a predicted sequence ŷ based at least in part on the combined latent activation representation h and the Fair Region vector.