Harms from Estrangement

Mitigation strategy

1. Mandate comprehensive regulatory oversight, including standardized procedures for measuring and disclosing the direct and indirect environmental consequences (e.g., energy, water, mineral use) of AI systems throughout their lifecycle. 2. Implement robust "human-in-the-loop" governance and clear accountability chains for AI deployed in environmentally sensitive domains to ensure human values, judgment, and non-neglected ecological interests are consistently integrated into automated decision-making. 3. Require the use of resource-efficient AI architectures, such as smaller language models (SLMs) for specialized tasks, and continuous operational performance optimization to minimize the energy and resource footprint of AI inference and data center operations.