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The integration of artificial intelligence (AI) into materials science has significantly accelerated discovery and optimization processes. Yet, it simultaneously amplifies long-standing epistemic vulnerabilities rooted in the systematic underrepresentation of negative results. Failed experiments, unstable material phases, and inaccurate predictions are often excluded from the published record, resulting in datasets that are skewed and shape AI model training and inference. This conceptual paper examines how epistemic gaps distort the dynamics of data generation, model development, and experimental validation in materials AI. By synthesizing literature on publication bias, model robustness, and uncertainty-aware learning, the study demonstrates how positive-only knowledge bases foster overconfident predictions, limit generalization, and obscure material boundary conditions. To address these challenges, the paper proposes a failure-aware epistemic learning framework that structurally integrates negative results into AI-driven materials discovery through recursive feedback structures, uncertainty modulation, and inclusive steering logics. Ethical reasoning situates this framework within principles of epistemic accountability, sustainability, and responsible innovation. By reinterpreting negative results as indispensable sources of information rather than peripheral artifacts, the paper advances a conceptual foundation for more resilient, transparent, and reliable AI applications in materials science.