Context Without Thickness : Why AI Can't Understand What It Learns

Abstract

From philosophical and scientific perspectives, the interpretation of phenomena is inseparable from context—a view more fundamental than classical approaches that tie cognition to the mind's structure or phenomena's essences. This paper traces a paradigmatic shift in cognition: from Nietzsche's perspectivism and rejection of absolute objectivity, through Husserl's "to the things themselves" and emphasis on lived experience, to Wittgenstein's "family resemblances" and Heidegger's distinction between "ready-to-hand" and "present-at-hand." Gadamer's fusion of horizons and Derrida's différance extended these ideas in interpretation and metaphysics critique, while statistics (via correlation) and fuzzy logic (via modeling ambiguity) operationalized the paradigm mathematically. The paper's innovation lies in synthesizing this unified trajectory toward embodiment in statistics, fuzzy logic, and artificial intelligence, revealing how AI—particularly machine learning—draws from these philosophical insights by categorizing via feature overlap without precise definitions. Yet this inheritance is paradoxical: AI operationalizes context but reduces it to mere correlation, producing what we term "context without thickness" —pattern recognition without phenomenological depth, cultural embeddedness, or historical situatedness. Through analysis of AI failures (the Clever Hans problem, adversarial vulnerabilities, and catastrophic loss of context in real-world applications), we demonstrate that the gap between statistical pattern recognition and genuine human context-awareness is not merely technical but fundamentally philosophical. Bridging this gap requires not better algorithms alone, but a reengagement with the very philosophical tradition that diagnosed the contextual nature of meaning itself.