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Autonomous and semi-autonomous laboratories represent a transformative paradigm in materials science, integrating artificial intelligence, robotics, and high-throughput experimentation to accelerate discovery and optimization processes. This review examines the conceptual foundations of these systems, including closed-loop optimization, machine learning algorithms, and modular hardware architectures. We explore their applications in areas such as alloy development, perovskite synthesis, and nanoparticle engineering, highlighting successes that have reduced discovery timelines from years to days. However, we also critically assess associated risks, including data quality issues, algorithmic biases, ethical concerns in resource allocation, and potential safety hazards from unsupervised operations. Drawing on recent advances, we propose balanced implementation strategies that maximize innovation while mitigating risks. The review underscores the need for interdisciplinary collaboration to realize the full potential of these technologies in addressing global materials challenges.

The convergence of machine learning, high-throughput computation, and autonomous experimentation has transformed materials discovery into an AI-directed process capable of closed-loop, data-driven innovation at unprecedented speed. This narrative review examines the computational and data-driven materials engineering ecosystem, with a specific focus on the governance, regulatory, and institutional oversight frameworks required to steward these capabilities responsibly. We synthesize developments in materials informatics, representation learning, graph neural networks, active learning, uncertainty quantification, and simulation–experiment integration, showing how these tools have enabled autonomous laboratories and inverse design. Particular attention is given to community-driven calls for standards, explainability, and scientific responsibility that have emerged alongside the technology. By integrating technical literature with explicit discussions of data governance, reproducibility, and ethical deployment, we articulate the need for structured institutional oversight models that span standards bodies, regulatory readiness, and multi-stakeholder governance regimes. These models must operate at the infrastructure level—embedding accountability into discovery pipelines rather than retrofitting them. The review positions institutional oversight not as a constraint on innovation but as an essential enabler that ensures AI-directed materials engineering delivers safe, equitable, and societally beneficial outcomes.