Dynamically monitoring variations in process explainability within a distributed computing environment

Abstract

The disclosed embodiments include computer-implemented systems and processes that dynamically monitor variations in process explainability of within a distributed computing environment. For example, an apparatus may obtain first and second explainability data associated with corresponding first and second temporal intervals, and based on the first and second explainability data, determine a value of a metric that characterizes a variation in the explainability of a machine-learning process between the first and second temporal intervals. When the metric value is inconsistent an exception criterion, the apparatus may obtain at least one additional value of the metric associated with a third temporal interval, and when the at least one additional metric value is inconsistent with the exception criterion, the apparatus may perform operations that modify at least one of (i) a value of a process parameter of the machine-learning process or (ii) a composition of an input dataset of the machine-learning process.