John D. Norton’s Material Theory of Induction challenges the assumption that inductive reasoning follows universal schemas, suggesting instead that specific material facts locally justify all induction. When applied to data classification, digital materiality, and epistemology, this framework reveals the inherent limitations of fixed classification systems, the role of individuation in structuring knowledge, and the epistemic uncertainties inherent in digital ontologies. Contemporary data classification systems are not neutral; they actively shape and reinforce existing social and structural inequalities. As algorithms learn from historical data, they inherit and perpetuate biases, often resulting in discriminatory outcomes. The philosophies of Gilbert Simondon and Yuk Hui provide critical insights into the evolving nature of technical objects and digital entities, highlighting the inadequacies of static classification. By integrating their theories, this study argues for a dynamic, context-sensitive approach to induction and classification that adapts to changing material and social conditions, ultimately enhancing fairness and predictive accuracy in decision-making systems.