Ethical and Sustainable Deployment of AI for Critical Mineral Extraction in the U.S.: A Multi-Objective Optimization Framework for Advancing Energy Transition and Environmental Stewardship

The rapid development of renewable energy systems has created an unprecedented demand for critical minerals, which places the United States at a crossroads of energy security, environmental sustainability and social responsibility. Traditionally, mining has focused on production efficiency, often leading to extensive environmental damage, displacement of communities and worker safety concerns, thereby undermining long-term sustainability goals. This paper introduces a multi-objective optimization model that combines Artificial Intelligence with ethical considerations and sustainable development goals, which offers a rigorous approach to responsible critical mineral mining across the United States. The paper conducts a systematic literature review to examine five key areas: 1) the vulnerabilities of the critical mineral supply chain and its geopolitical implications; 2) the environmental and social impacts of conventional mining; 3) the role of Artificial Intelligence in the mining industry; 4) ethical principles guiding responsible AI management; and 5) multi-objective optimization of decision-support systems. Synthesis of recent empirical research shows that AI technologies improve ore-grade prediction accuracy by about 30 percent, however, geopolitical risks significantly influence mineral price volatility and supply stability. The analysis reveals that 54% of global mining operations are located on Indigenous land, often without permission and that by 2035, automation could displace 30-45% of the mining workforce. The proposed framework addresses the complex trade-offs among production efficiency, environmental protection, social equity and economic viability, using advanced optimization algorithms. These algorithms incorporate environmental monitoring, community impact considerations and regulatory compliance, which ensures comprehensive decision-making.