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The integration of computational tools and data-driven methodologies has transformed materials engineering, enabling accelerated discovery through AI-assisted pipelines that link data acquisition, model training, and experimental validation. In this paradigm, materials informatics leverages vast datasets from high-throughput computations and multimodal sources to inform design decisions, yet inherent feedback dynamics often introduce biases that steer exploration trajectories in unintended ways. This conceptual manuscript identifies a critical gap in understanding how data-model-experiment loops can self-reinforce certain pathways, leading to narrowed exploration spaces and amplified discovery biases. To address this, we introduce the Feedback Steering Framework (FSF), a systems-level architecture that interprets the interplay between data representations, model inferences, and iterative design cycles. The framework elucidates mechanisms such as reinforcement discovery bias, where initial data patterns perpetuate model preferences, and exploration narrowing, wherein computational steering logics constrain the search space over successive iterations. By conceptualizing these dynamics, FSF provides insights into optimizing AI-guided materials exploration for broader epistemic coverage. Implications extend to computational materials science ecosystems, including enhanced uncertainty management in autonomous systems and more robust inverse design strategies, ultimately fostering resilient infrastructures for next-generation materials innovation. This work underscores the need for interpretive tools that balance computational efficiency with comprehensive discovery potential in data-steered environments.