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In the rapidly expanding domain of artificial intelligence applied to materials science, default assumptions embedded within machine learning pipelines—ranging from software library choices and architectural presets to data preprocessing routines and evaluation protocols—are routinely treated as neutral, inconsequential background elements that require no explicit justification. Yet these defaults operate as hidden parameters, subtly yet powerfully constraining the hypothesis space, directing optimization trajectories, and ultimately shaping the predictive behavior of models in ways that rival or even exceed the influence of explicitly tuned parameters, as theoretical analyses of deep networks have long emphasized. This paper advances the theoretical claim that default assumptions in materials AI function as implicit priors, encoding unacknowledged inductive biases that propagate through every stage of a pipeline and determine what counts as a valid or reliable prediction about material properties. Building directly on foundational examinations of inductive bias, we distinguish defaults from both explicit parameters and tunable hyperparameters, develop a taxonomy of four primary default types specific to materials informatics, and derive corollaries concerning the epistemic consequences of unexamined defaults for model comparison, reproducibility, and knowledge transfer. We further examine why such defaults persist—owing to cognitive convenience, historical path dependence, and systematic attribution errors—and clarify their subtle yet critical relation to formal Bayesian priors, while noting that understanding deep learning requires rethinking generalization when defaults remain hidden. The analysis culminates in concrete implications for practice, proposing that defaults must be elevated to first-class objects of documentation, justification, and sensitivity analysis if materials AI is to achieve genuine epistemic transparency and scientific robustness. By theorizing defaults as hidden parameters, this work identifies an overlooked dimension of model epistemology in materials science and offers a conceptual framework for making the invisible visible.