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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.

In the rapidly evolving field of materials science, artificial intelligence (AI) models have become integral to accelerating discovery and design processes. Yet, their evaluation often relies on simplistic accuracy measures that overlook the broader decision-making contexts. This conceptual paper develops a novel decision-theoretic framework for assessing materials AI models, integrating utility considerations, risk dynamics, and epistemic uncertainties to provide a more holistic understanding of model performance. By synthesizing recent literature on AI applications in materials science and decision theory, the framework interprets model outputs not merely as predictions but as inputs to decision processes where trade-offs between precision, computational efficiency, and real-world applicability shape outcomes. It explores analytical implications, including how utility-based evaluations reveal the interaction between model reliability and stakeholder priorities, fostering systems-level insights into AI’s role in sustainable materials innovation. Ethical reasoning is woven throughout, highlighting epistemic challenges in interpreting model behaviors under uncertainty. This approach steers away from isolated metric assessments toward integrative evaluations that align AI capabilities with the multifaceted demands of materials engineering, ultimately enhancing the trustworthiness and utility of AI-driven advancements. The framework’s interpretive lens offers pathways to refine evaluation practices, ensuring that AI models contribute meaningfully to decision-making in high-impact applications.