Dynamically monitoring variations in process explainability within a distributed computing environment

A computer-implemented method, comprising:

• based on data characterizing an explainability of a machine-learning or artificial-intelligence process, determining, using at least one processor, a first value of a metric that characterizes a variation in the explainability of the machine-learning or artificial-intelligence process between a first temporal interval and a second temporal interval;
• when the first metric value is inconsistent with an exception criterion, obtaining, using the at least one processor, a second value of the metric that characterizes an additional variation in the explainability of the machine-learning or artificial-intelligence process between the first temporal interval and a third temporal interval;
• and
• when the second metric value is inconsistent with the exception criterion, performing operations, using the at least one processor, that modify at least one of (i) a value of a process parameter that characterizes the machine-learning or artificial-intelligence process or (ii) a composition of an input dataset associated with the machine-learning or artificial intelligence process.

A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising:

• based on data characterizing an explainability of a machine-learning or artificial-intelligence process, determining a first value of a metric that characterizes a variation in the explainability of the machine-learning or artificial-intelligence process between a first temporal interval and a second temporal interval;
• when the first metric value is inconsistent with an exception criterion, obtaining a second value of the metric that characterizes an additional variation in the explainability of the machine-learning or artificial-intelligence process between the first temporal interval and a third temporal interval;
• and
• when the second metric value is inconsistent with the exception criterion, performing operations that modify at least one of (i) a value of a process parameter that characterizes the machine-learning or artificial-intelligence process or (ii) a composition of an input dataset associated with the machine-learning or artificial intelligence process.