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The integration of machine learning, robotics, and high-performance computing has transformed computational and data-driven materials engineering, shifting discovery from sequential human-led campaigns to autonomous, closed-loop pipelines capable of evaluating thousands of candidates per cycle. This paradigm delivers unprecedented throughput, yet it simultaneously disperses decision authority across data pipelines, inference engines, and robotic agents, creating a structural dilution of responsibility that existing frameworks have not systematically addressed. Current literature excels at accelerating prediction, synthesis, and characterization but treats governance as an external overlay rather than an intrinsic computational dynamic. The result is a growing epistemic risk: high-velocity discovery without traceable stewardship. This conceptual manuscript reframes throughput as a governance problem. We synthesize the data-driven materials ecosystem and autonomous laboratory architectures to expose how responsibility fragments across layered pipelines. To resolve this, we introduce the Dilution Cascade Framework, an original systems model that maps accountability propagation through data–model–discovery layers, formalizes feedback steering logics, and identifies computational interventions to restore traceability without sacrificing velocity. The framework offers infrastructure-level insights for embedding governance in next-generation autonomous platforms. Its implications extend to the design of materials innovation ecosystems that remain both high-throughput and epistemically accountable, ensuring that accelerated discovery serves as a foundation for responsible scientific infrastructure rather than a vector for diffused agency.