Computer systems and methods for explaining predictions of machine-learning models

A non-transitory computer-readable medium, wherein the non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing platform to:

• perform a plurality of explanation runs for explaining a prediction rendered by a machine learning model that is configured to receive, as input, (i) an embedding produced by a graph-based model based on a data graph comprising nodes interconnected by edges, wherein the nodes are associated with values for a set of nodal features, and (ii) a set of model features, wherein each explanation run involves:
• (i) identifying (a) a first local search space for a model-feature explanation component that identifies a subset of the set of model features, (b) a second local search space for a subgraph explanation component that identifies a subgraph of the data graph, and (c) a third local search space for a nodal-feature explanation component that identifies a subset of the set of nodal features;
• (ii) navigating the first, second, and third local search spaces to determine a respective candidate explanation that satisfies an explanation rule, wherein the respective candidate explanation comprises (a) a respective subset of the set of model features, (b) a respective subgraph of the data graph, and (c) a respective subset of the set of nodal features;
• and
• (iii) determining a respective explanation score for the respective candidate explanation;
• based on the respective explanation scores for the respective candidate explanations, determine a given explanation for the prediction comprising a given subset of the set of model features, a given subgraph of the data graph, and a given subset of the set of nodal features;
• and
• transmit, to a consumer system, data defining the given explanation and thereby cause an indication of the given explanation to be presented at a user interface of the consumer system.

A method carried out by a computing platform, the method comprising:

• performing a plurality of explanation runs for explaining a prediction rendered by a machine learning model that is configured to receive, as input, (i) an embedding produced by a graph-based model based on a data graph comprising nodes interconnected by edges, wherein the nodes are associated with values for a set of nodal features, and (ii) a set of model features, wherein each explanation run involves:
• (i) identifying (a) a first local search space for a model-feature explanation component that identifies a subset of the set of model features, (b) a second local search space for a subgraph explanation component that identifies a subgraph of the data graph, and (c) a third local search space for a nodal-feature explanation component that identifies a subset of the set of nodal features;
• (ii) navigating the first, second, and third local search spaces to determine a respective candidate explanation that satisfies an explanation rule, wherein the respective candidate explanation comprises (a) a respective subset of the set of model features, (b) a respective subgraph of the data graph, and (c) a respective subset of the set of nodal features;
• and
• (iii) determining a respective explanation score for the respective candidate explanation;
• based on the respective explanation scores for the respective candidate explanations, determining a given explanation for the prediction comprising a given subset of the set of model features, a given subgraph of the data graph, and a given subset of the set of nodal features;
• and
• transmitting, to a consumer system, data defining the given explanation and thereby causing an indication of the given explanation to be presented at a user interface of the consumer system.