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In the evolving landscape of computational materials engineering, artificial intelligence (AI) has emerged as a pivotal orchestrator, directing exploratory pipelines from data curation to predictive modeling and synthesis validation. This integration, while accelerating discovery, introduces profound control asymmetries wherein algorithmic decisions preempt human oversight, often without explicit consent mechanisms embedded in the workflow. Such asymmetries manifest as latent divergences between intended exploratory intents and AI-mediated trajectories, potentially skewing material property predictions and optimization paths in unintended directions. Drawing from systems-level analyses of machine learning applications in solid-state materials science, generative sampling strategies, and active learning protocols, this manuscript conceptualizes these dynamics through an original interpretive framework: the Asymmetric Steering Topology (AST). The AST delineates layered interactions across data ingestion, model inference, and discovery actuation, highlighting feedback loops that amplify epistemic risks in unconsented steering. By interpreting these asymmetries as infrastructural tensions—between representational fidelity and inferential autonomy—the framework elucidates how AI-directed exploration can inadvertently prioritize computational efficiency over exploratory equity. Implications for the field include reimagined pipeline architectures that integrate consent-aware safeguards, fostering more equitable human-AI symbiosis in materials informatics. This conceptual synthesis advances understanding of discovery steering logics, urging a shift toward epistemically resilient infrastructures that balance algorithmic prowess with interpretive sovereignty in data-driven materials engineering.