Use when auditing the data and foundational design of an AI/ML personnel assessment — Components 1-2 of the Landers & Behrend (2023) framework. Covers input-data population, sampling, range restriction, and incumbent-vs-applicant generalizability; and model design: how the criterion ("ground truth") is defined and its construct validity, why each predictor/feature was included, and whether choices were theory-driven or empirically derived. Triggers: "audit training data", "is the training sample representative", "ground truth validity", "why are these features predictors", "criterion definition in AI hiring", "range restriction in algorithm training data".
