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The integration of artificial intelligence (AI) into materials science represents a profound epistemic shift, challenging longstanding assumptions about the nature and validation of scientific knowledge. Rather than merely accelerating computational tasks, AI reconfigures the epistemological landscape by generating outputs that blur the boundaries between data, inference, and insight. This paper diagnoses a central problem: “knowledge inflation,” where AI’s predictive prowess is prematurely equated with genuine understanding, leading to overconfidence in materials-related decisions. Tensions arise between the opacity of AI-driven predictions and the demands for explanation and mechanistic clarity inherent to materials science, where structure-property relationships and causal processes have traditionally grounded epistemic warrant. Such discrepancies risk undermining the reliability of knowledge claims in domains like alloy design and sustainable material selection. To address this, we propose a reframed epistemic framework tailored to materials AI, centered on actionability as the criterion for knowledge: what can be responsibly acted upon in practical contexts. This includes a novel typology distinguishing epistemic categories of AI outputs, from mere predictive signals to robust decision warrants, with conditions for elevation between them. By emphasizing responsibility and scope, this reframing aims to safeguard epistemic integrity while harnessing AI’s potential.

In the domain of applied artificial intelligence (AI) for materials science, uncertainty emerges as a pivotal signal that informs design decisions, yet its conceptual interpretation remains underexplored. This paper delineates uncertainty as the lack of complete knowledge about a system's state or outcomes, distinct from confidence, which reflects a model's self-assessed reliability in predictions; risk, which weights uncertainty by potential consequences; and actionability, which denotes the warrant for proceeding with design actions based on interpreted signals. Traditional approaches often conflate these concepts, leading to suboptimal decisions in materials discovery and optimization. For instance, high confidence in AI predictions may not equate to low risk in high-stakes applications like alloy design for extreme environments, where epistemic gaps could amplify failures. This conceptual manuscript proposes a decision-theoretic framework, the Uncertainty-to-Action Map, that translates uncertainty types—epistemic, aleatory, and semantic—into risk postures and subsequent action classes, such as screening candidates, prioritizing explorations, deferring judgments, redesigning models, stopping pursuits, hedging bets, or diversifying portfolios. By incorporating gates for stake assessment, ambiguity detection, domain scope evaluation, cost asymmetry analysis, and stopping logic, the framework mitigates failure modes like overconfidence and decision paralysis. This model fosters a nuanced view, emphasizing that uncertainty, when properly interpreted, serves as a design asset rather than a hindrance, promoting robust AI-assisted materials innovation.