  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.
