Techniques to perform global attribution mappings to provide insights in neural networks

A non-transitory processor-readable medium embodying a credit risk assessment program for global attribution mapping (GAM), the program to cause a processor:

• generate local credit risk assessment attributions for a set of credit risk assessment samples;
• normalize each local credit risk assessment attribution of the local credit risk assessment attributions;
• compare each pair of normalized local credit risk assessment attributions, and assigning a rank distance thereto proportional to a degree of ranking differences between the pair of normalized local credit risk assessment attributions;
• apply a medoids clustering algorithm to assign each local credit risk assessment attribution to a cluster having a medoid which is closest to the local credit risk assessment attribution based on the ranking distances, and which updates each cluster medoid by minimizing pairwise dissimilarity within a same cluster, and repeating running of the medoids clustering algorithm to adjust the local credit risk assessment attributions into converged clusters representing sub-populations having similar attributes;
• and
• utilize medoids and sizes of the converged clusters, to derive GAM insight into one or more of the local credit risk assessment attributions, wherein the GAM insight comprises a bias in a neural network, one or more features to include in the neural network, or a combination thereof.

A system to perform global attribution mapping (GAM), comprising:

• a storage system comprising a set of credit risk assessment data samples;
• memory;
• and
• one or more processors coupled with the memory and storage system, the one or more processors configured to:
• determine, from the storage system, the set of credit risk assessment data samples;
• generate local credit risk assessment attributions for the set of credit risk assessment samples, and each local credit risk assessment attribution includes a rank and a weight of an association between a particular feature and a particular prediction predicted by a neural network;
• normalize each local credit risk assessment attribution;
• compare each pair of normalized local credit risk assessment attributions;
• assign a rank distance thereto proportional to a degree of ranking differences between the pair of normalized local credit risk assessment attributions based on the comparison;
• apply clustering algorithm to generate clusters of the local risk assessment attributions, wherein the clustering algorithm utilizes the ranking distances for each pair of normalized local credit risk assessment attributions to assign each local credit risk assessment attribution to one of one or more clusters, and each cluster having a medoid;
• generate global attributions, wherein each global attribution corresponds to a medoid of one of the clusters;
• and
• determine a bias in the neural network, one or more features include in the neural network, or a combination thereof based on the global attributions.