Towards Environmentally Equitable AI via Geographical Load Balancing

Fueled by the soaring popularity of foundation models, the accelerated growth of artificial intelligence (AI) models’ enormous environmental footprint has come under increased scrutiny. While many approaches have been proposed to make AI more energy-efficient and environmentally friendly, environmental inequity — the fact that AI’s environmental footprint can be disproportionately higher in certain regions than in others — has emerged, raising social-ecological justice concerns. This paper takes a first step toward addressing AI’s environmental inequity by fairly balancing its regional environmental impact. Concretely, we focus on the carbon and water footprints of AI model inference and propose equity-aware geographical load balancing (eGLB) to explicitly minimize AI’s highest environmental cost across all the regions. The consideration of environmental equity creates substantial algorithmic challenges as the optimal GLB decisions require complete offline information that is lacking practice. To address the challenges, we introduce auxiliary variables and optimize GLB decisions online based on dual mirror descent. In addition to analyzing the performance of eGLB theoretically, we run trace-based empirical simulations by considering a set of geographically distributed data centers that serve inference requests for a large language AI model. The results demonstrate that existing GLB approaches may amplify environmental inequity while eGLB can significantly reduce the regional disparity in terms of carbon and water footprints.