System and method for removing biases within a distributable model

A system for improving a distributable model with distributed data, comprising:

• a distributable model source comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to:
• store a plurality of instances of a distributable model;
• and
• serve instances of the distributable model;
• and
• a directed computational graph module comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to:
• receive an instance of the distributable model from the computing system;
• operate a directed computational graph comprising a plurality of data transformation nodes;
• remove a plurality of data biases within the instance of the distributable model using at least a portion of the plurality of data transformation nodes;
• create a cleansed dataset based on the instance of the distributable model and the removed biases;
• wherein the biases are automatically corrected by the distributed computational graph to generalize the dataset for use in a distributable model;
• train the instance of the distributable model with the cleansed dataset;
• and
• generate an update report based upon which remote instances of the distributable model may be adjusted to take into account the training of the instance of the distributable model using the using the cleansed dataset.