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Self-driving laboratories have emerged as a cornerstone of computational and data-driven materials engineering, fusing automated high-throughput experimentation with machine-learning-driven decision engines to compress discovery timelines from years to weeks. This paradigm shift reconfigures the materials pipeline into a closed-loop system in which data generation, model inference, and experimental steering operate with minimal human intervention. Yet the very autonomy that accelerates discovery simultaneously obscures the epistemic foundations of the knowledge it produces. Failures—whether arising from underrepresented chemical spaces, model extrapolation beyond training distributions, or unacknowledged aleatoric–epistemic uncertainty boundaries—often remain latent until downstream validation, eroding trust in autonomous outputs. Current uncertainty quantification and explainability techniques, while technically sophisticated, are typically deployed in isolation and rarely propagate failure signals across the full discovery stack. We articulate a conceptual architecture, the Epistemic Visibility and Accountability Framework (EVAF), that treats failure not as an anomaly to be minimized but as a structured signal to be surfaced and attributed at every layer of the self-driving pipeline. By integrating multi-scale representation tracking, inference-trace logging, and risk-propagation mapping, EVAF establishes a computational substrate for epistemic accountability: the systematic assignment of responsibility for knowledge claims to specific data, model, or orchestration components. The framework reframes self-driving systems from opaque optimizers into transparent epistemic engines, enabling materials engineers to maintain intellectual oversight without sacrificing autonomy. Its implications extend to infrastructure design, regulatory readiness for autonomous discovery platforms, and the long-term reliability of data-intensive materials science.