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This review examines the literature on ethical frameworks for artificial intelligence (AI) applied to materials science and discovery, synthesizing insights from 31 peer-reviewed publications spanning 2017 to 2026 to trace the evolution from high-level principles to practical implementation. The methodology involved a systematic search across Web of Science, Scopus, arXiv, and PhilPapers using targeted strings such as “ethics AI materials science,” “responsible AI materials discovery,” “ethical framework AI science,” “dual use materials AI,” “AI ethics principles materials,” “governance AI materials research,” “justice AI materials discovery,” and “value alignment materials AI,” with inclusion limited to peer-reviewed works directly addressing ethical dimensions in scientific or materials contexts, yielding 31 core references after PRISMA-style screening of over 500 initial results. Major ethical principles for AI—beneficence, non-maleficence, autonomy, justice, explicability, and sustainability—are surveyed as foundational guides originally developed in broader AI ethics literature but rarely adapted to materials-specific applications. The current state of materials AI literature reveals a predominant focus on technical acceleration of discovery, with explicit ethical engagement appearing in fewer than 20% of surveyed works and often limited to passing mentions rather than systematic analysis. Materials-specific ethical challenges, including dual-use risks in weaponizable materials, environmental harms from resource-intensive AI-driven synthesis, equity gaps in global access to discoveries, labor displacement through automation, intellectual property ambiguities, and intergenerational justice concerns, remain largely unaddressed despite the field’s rapid growth. Significant gaps persist in operationalizing principles, developing governance mechanisms, and providing domain-tailored guidance, underscoring an urgent need for actionable recommendations to bridge the principles-practices divide and foster responsible materials AI innovation that prioritizes societal benefit, sustainability, and justice.