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Multi-scale materials AI depends on abstraction as a necessary but inherently risky operation: researchers must simplify systems spanning electronic, atomic, microstructural, and macroscopic scales to achieve computational tractability, yet every simplification discards degrees of freedom, interactions, or information whose relevance cannot be known a priori. Abstraction, therefore, stands at the heart of every coarse-grained molecular-dynamics run, every surrogate model, and every continuum approximation, yet the epistemic costs of these choices remain largely unexamined. Without explicit justification, abstracted models risk producing predictions that appear accurate within narrow validation regimes while failing catastrophically when deployed on new tasks, new materials, or new operating conditions. This paper argues that abstraction cannot be taken for granted and instead requires a principled theory of justifiable abstraction. The proposed theory rests on three core principles—task-relative justification, information-preservation criterion, and multi-scale validation—supported by five operational criteria that together allow researchers to decide, for any given modeling context, whether an abstraction is defensible or whether higher-fidelity reference calculations must be retained. The framework further distinguishes four canonical types of abstraction (spatial, temporal, compositional, and physical) that appear repeatedly across the literature on multi-scale machine learning for materials. By making justification explicit and evaluable, the theory shifts multi-scale materials AI from an ad-hoc practice to a disciplined epistemic activity, ensuring that computational gains do not come at the expense of scientific reliability or technological trustworthiness. The implications extend beyond individual papers to the design of benchmarks, the standards of peer review, and the very architecture of future hierarchical modeling platforms.