Method, Not Stance: Structural Disclosure as an Operative Methodology for Human–AI Division of Labor

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Current debates on human–AI division of labor mostly state a stance: AI handles routine induction and prediction, while humans retain judgment under novelty. This stance is not new, but it remains under-operationalized. Frameworks such as human-in-the-loop specify that humans should supervise or intervene, yet they rarely identify which part of "the human" is non-delegable, how that part is located in a concrete task, or how it can be preserved once the practitioner is gone. This paper presents structural disclosure (Xianying) as an operative methodology rather than a normative claim. It replaces the vague human/AI boundary with a burden-bearing criterion: a judgment is non-delegable when a human occupies a pressure-bearing position and must bear the consequences of being wrong. AI may perform inductive computation and may assist in elaborating past cases, but it cannot occupy the burden-bearing position, because it does not undergo the consequences. The method operates through three rules: anchoring the context, recording the complete decision structure, and ensuring the case can be transferred. The third rule is tested at three levels—comprehension, analogous reapplication, and creative activation in structurally isomorphic but novel situations—preferably by independent third-party practitioners. The paper distinguishes structural disclosure from induction, tacit-knowledge theory (Polanyi), hermeneutics (Gadamer), and legal precedent, and demonstrates its application to governance wisdom and crisis judgment. It thereby converts the human-in-the-loop slogan into an identifiable, recordable, and teachable procedure.