electronically collecting pre-hire information from a plurality of applicants wherein at least some of the pre-hire information is collected from at least one of the applicants who responds directly on an electronic device to provide pre-hire applicant responses to questions;

collecting post-hire information for the applicants based on job performance of the applicants after hire;

via information-theoretic feature selection, choosing questions from the pre-hire information as features for which respective pre-hire applicant responses serve as inputs to the model, wherein the information-theoretic feature selection comprises identifying at least one higher-order interaction comprising a set of a plurality of questions having higher predictive power than a sum of predictive powers of individual questions in the set, wherein the higher-order interaction exhibits a synergy between the set of the plurality of questions having higher predictive power;

from the pre-hire information and the post-hire information, training an artificial intelligence-based predictive model in a computer-readable medium with observed pre-hire applicant responses for the chosen features, wherein the artificial intelligence-based predictive model is operable to generate one or more job performance criteria predictions based at least on input pre-hire information from new applicants corresponding to the chosen features, whereby the one or more job performance criteria predictions are usable as a basis for a hiring recommendation or other employee selection information;

deploying the model, wherein deploying comprises converting the model into command code and providing an operational applicant processing system; and

conducting performance tuning for the model, wherein performance tuning comprises continuing data collection, monitoring sample size as incoming data accumulates, and repeating feature selection.