Towards Environmentally Equitable AI via Geographical Load Balancing
This addresses environmental justice concerns for disadvantaged regions affected by AI's disproportionate environmental impacts, representing an incremental step in making AI more equitable.
The paper tackles the problem of environmental inequity in AI by proposing equity-aware geographical load balancing to reduce regional disparities in carbon and water footprints during model inference, demonstrating that their approach significantly reduces these disparities compared to existing methods.
Fueled by the soaring popularity of large language and 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 balancing its regional negative environmental impact. Concretely, we focus on the carbon and water footprints of AI model inference and propose equity-aware geographical load balancing (GLB) to explicitly address AI's environmental impacts on the most disadvantaged regions. We run trace-based simulations by considering a set of 10 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 our proposed equity-aware GLB can significantly reduce the regional disparity in terms of carbon and water footprints.