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Materials AI systems, which apply machine learning techniques to accelerate the discovery, design, and optimization of new materials, are not value-neutral despite frequent claims of technical objectivity. Every design choice—ranging from the selection of training datasets and the formulation of objective functions to the prioritization of target properties and the definition of success metrics—necessarily embeds specific values, even when researchers present their work as purely data-driven or performance-oriented. Yet these embedded values remain largely unexamined in the current literature on materials informatics and autonomous materials research. The values at stake span multiple dimensions: epistemic values such as accuracy, reproducibility, and interpretability that underpin scientific validity; ethical values including safety, fairness, and accountability to prevent harm; social values like equity and benefit-sharing that address who gains from new materials; economic values focused on efficiency, cost-effectiveness, and scalability; and environmental values centered on sustainability, non-toxicity, and circularity. Value-sensitive design (VSD), originally developed within human-computer interaction as a principled approach to technology development, provides a systematic framework for making these values visible, negotiable, and actionable rather than leaving them implicit or unacknowledged. Building directly on established VSD foundations, this paper proposes a conceptual framework tailored specifically to materials AI contexts. The framework includes five integrated components—value identification, stakeholder mapping, value operationalization, design translation, and value evaluation—alongside a typology of five value categories and explicit guidance for navigating common value tensions. By adapting VSD principles to the unique challenges of materials discovery, the framework offers a pathway to responsible innovation that aligns technical capabilities with broader human and environmental priorities. Ultimately, adopting value-sensitive practices in materials AI will help ensure that AI-augmented materials research contributes not only to faster discovery but also to more equitable, sustainable, and ethically sound outcomes for society and the planet.