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The integration of artificial intelligence (AI) into materials science, often referred to as Materials AI, has revolutionized the field by enabling accelerated discovery, design, and optimization of new materials. This narrative review explores the governance and responsible use of Materials AI, focusing on standards, transparency, and risk management. Drawing on peer-reviewed literature, we examine the evolution of AI applications in materials science, ethical considerations, and the need for robust frameworks to ensure accountable deployment. Key themes include the ethical implications of data bias and intellectual property in AI-driven materials discovery; the development of standards for model validation and interoperability; mechanisms to enhance transparency in black-box AI models; and strategies to identify and mitigate risks, such as model unreliability and societal impacts. The review highlights how autonomous experimentation systems and machine learning techniques have transformed materials research, while underscoring the importance of reflexive governance to address potential harms. Objectives include synthesizing current practices, identifying gaps in responsible AI adoption, and proposing pathways for sustainable integration. By fostering transparency and risk-aware approaches, Materials AI can contribute to societal benefits, such as advancing energy materials and sustainable manufacturing, while minimizing ethical pitfalls. This work emphasizes the interdisciplinary nature of responsible Materials AI and calls for collaboration among scientists, policymakers, and ethicists to establish trustworthy systems.

The rapid evolution of computational and data-driven materials engineering has ushered in an era where self-driving laboratories (SDLs) promise to transform materials discovery by integrating automation, machine learning, and high-throughput experimentation into cohesive governance architectures. These architectures orchestrate the interplay between data generation, model training, and decision-making processes to enable closed-loop optimization in materials design. This review synthesizes recent advancements in SDL governance, focusing on how computational workflows—encompassing materials informatics, graph neural networks, representation learning, and uncertainty quantification—facilitate autonomous systems in addressing complex materials challenges. We examine the foundational elements of data-driven ecosystems, including multimodal datasets and simulation-experiment integration, and explore active learning strategies that balance exploration and exploitation in inverse design paradigms. Key governance components, such as orchestration platforms like ChemOS 2.0 and Bayesian active learning frameworks, are analyzed for their role in accelerating discovery cycles. By integrating perspectives from high-impact studies, we highlight how these architectures mitigate inefficiencies in traditional trial-and-error approaches, enabling scalable, reproducible materials innovation. The review positions SDL governance as a critical infrastructure for future materials engineering, emphasizing systems-level integration over isolated techniques. Ultimately, it underscores the potential of these architectures to democratize access to advanced materials development while identifying pathways for enhanced interoperability and robustness in computational ecosystems.