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The integration of artificial intelligence (AI) into materials science has ushered in an era of semi-autonomous systems that accelerate discovery through predictive modeling, high-throughput screening, and adaptive experimentation. These systems offer substantial promise for addressing global challenges in energy, sustainability, and advanced manufacturing; however, their reliance on data-driven inference introduces risks related to bias propagation, epistemic uncertainty, and misalignment with scientific values. Conventional approaches treat human oversight primarily as an external corrective mechanism—post hoc monitoring or intervention in response to model outputs. This paper proposes a conceptual reframing wherein human oversight is repositioned as an intrinsic element of system design. Rather than viewing control as supervision layered atop an autonomous core, oversight is conceptualized as deliberate architectural choices that embed human judgment into the foundational structure of semi-autonomous materials AI. Drawing on literature from materials informatics, data bias mitigation, explainable AI, and human-AI collaboration, the proposed framework delineates three interdependent dimensions: epistemic boundary-setting, value-aligned modulation, and adaptive reflexivity. This reframing shifts the discourse from mitigating human absence to engineering human presence, fostering systems that are inherently more robust, interpretable, and aligned with the normative goals of scientific inquiry. By reconceptualizing oversight as design, the framework offers a pathway to responsible integration of AI in materials discovery without presupposing full autonomy or diminishing human agency.

The progressive integration of artificial intelligence into materials discovery has introduced systems capable of generating hypotheses autonomously. Yet, the problem of scientific autonomy remains largely unexamined as a distinct failure mode within the field. Scientific autonomy is defined here as the degree to which an AI system independently performs hypothesis generation, experimental design, or result interpretation without meaningful human oversight or intervention. This concept must be rigorously distinguished from mere automation, which can still preserve human decision rights. This autonomy introduces multiple mechanisms of failure—including opacity of internal reasoning processes, speed mismatches between AI generation rates and human cognitive capacities, goal misalignments between optimization objectives and epistemic goals, and authority erosion wherein human scientists increasingly defer to machine outputs—each of which undermines the foundational norms of scientific inquiry in materials science. The analysis further articulates a typology of four specific autonomy failure modes—hypothesis proliferation, pathological focus, unaccountable hypotheses, and epistemic lock-in—that manifest uniquely in materials AI contexts such as self-driving laboratories and closed-loop Bayesian optimizers. Detection principles are proposed to identify when autonomy becomes problematic, while mitigation principles emphasize deliberate design strategies to restore appropriate human control. By framing scientific autonomy as a core failure mode rather than an inevitable byproduct of progress, this paper argues for a recalibration of current practices in automated materials hypothesis generation, ensuring that technological advancement does not come at the expense of human epistemic authority or scientific understanding. Ultimately, the work calls for explicit attention to autonomy levels in the design and deployment of materials AI systems to safeguard the integrity of discovery processes.