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Human-in-the-loop (HITL) approaches are increasingly invoked in materials artificial intelligence (AI) as a presumed remedy for unreliable models, opaque predictions, and domain-shift failures. Yet “including a human” often functions as a rhetorical assurance rather than a precise scientific claim, masking the fact that humans participate in materially different ways: as labelers, judges, curators, constraint designers, hypothesis framers, risk owners, and accountability anchors. This conceptual manuscript argues that HITL is not a single method but a family of epistemic and governance roles that shape what an AI output means, what it can justify, and what actions it can responsibly warrant. Building on recent developments in materials informatics, active learning, uncertainty quantification, interpretable machine learning, and scientific machine learning, we synthesize a theory-first view of human involvement as a structured intervention in the AI-to-decision pathway rather than an informal override mechanism. We introduce a novel taxonomy that distinguishes (i) where humans intervene in the pipeline (data, representation, model, evaluation, decision), (ii) what kind of authority they exert (epistemic, normative, operational), and (iii) how their involvement changes the legitimacy of downstream claims under differing stakes. The resulting framework replaces HITL hype with a falsifiable conceptual vocabulary for designing responsibility, reliability, and restraint in materials AI.