DISTRIBUTED ARCHITECTURE FOR EXPLAINABLE AI MODELS

A method, and system for a distributed artificial intelligence architecture may be shown and described. An embodiment may present an exemplary distributed explainable neural network (XNN) architecture, whereby multiple XNNs may be processed in parallel in order to increase performance. The distributed architecture may include a parallel execution step which may combine parallel XNNs into an aggregate model by calculating the average (or weighted average) from the parallel models. A distributed hybrid XNN/XAI architecture may include multiple independent models which can work independently without relying on the full distributed architecture. An exemplary architecture may be useful for large datasets where the training data cannot fit in the CPU/GPU memory of a single machine. The component XNNs can be standard plain XNNs or any XNN/XAI variants such as convolutional XNNs (CNN-XNNs), predictive XNNS (PR-XNNs), and the like, together with the white-box portions of grey-box models like INNs.