1. A non-transitory medium storing code representing a plurality of processor-executable instructions, the code comprising code to cause the processor to: produce, via a trained machine learning model implemented by an asynchronous and interactive machine learning system, a predicted value for a membership relation between a data object and a target tag; display, via a user interface, the data object and the target tag; indicate a non-empty set of identified sections of one or more attribute values of the data object supporting the membership relation between the data object and the target tag, the code to indicate the non-empty set of identified sections of the one or more attribute values including code to: determine a set of sections of the one or more attribute values of the data object supporting the membership relation, each section from the set of sections paired with a pre-salience value calculated as a function of a stochastic gradient descent between the data object and the target tag; select from the set of sections of the one or more attribute values of the data object at least one section paired with a pre-salience value greater than a salience threshold value; and render at the user interface, a graphical indicator highlighting the at least one section paired with a section salience value; enable a user, via the asynchronous and interactive machine learning system to provide at the user interface a tag signal with feedback to the trained machine learning model, the tag signal indicative of an accept input, a dismiss input, an input to modify the graphical indicator highlighting the at least one section, or an input to modify the section salience value; and re-train the trained machine learning model via the asynchronous and interactive machine learning system, based at least in part on the tag signal causing an accuracy improvement of the trained machine learning model.