Human in the Loop, or Perceived Oversight? The Psychological Inference That Drives AI Credibility
通过两项实验研究，发现人类监督AI反而降低可信度，而感知到的监督充分性才是提升可信度的关键心理机制。
ABSTRACT Industry practice and AI governance frameworks increasingly treat human oversight of AI systems as a core trust‐building mechanism. Yet the specific psychological inference through which human‐in‐the‐loop (HITL) systems build credibility remains unidentified. We address this gap across two experiments in AI‐generated investment research. In Study 1, we manipulate the disclosed source of an identical report (human, black‐box AI, human‐supervised AI, or glass‐box AI) and find that source labels exert only small effects on credibility and no effect on investment behavior. Critically, human‐supervised AI produces the lowest credibility of any condition, contradicting the assumption that adding a human is always trust‐positive. In Study 2, we decompose HITL into five theoretically grounded candidate signals through which perceived source could shape credibility. Perceived oversight adequacy, the inference that “someone competent reviewed this work before I saw it,” emerges as the dominant mediator of the source‐to‐credibility relationship. The findings reframe the HITL question from whether to add a human to how to signal that the analytical process was sound.