  The sharply increasing sizes of artificial intelligence (AI) models come with
significant energy consumption and environmental footprints, which can
disproportionately impact certain (often marginalized) regions and hence create
environmental inequity concerns. Moreover, concerns with social inequity have
also emerged, as AI computing resources may not be equitably distributed across
the globe and users from certain disadvantaged regions with severe resource
constraints can consistently experience inferior model performance.
Importantly, the inequity concerns that encompass both social and environmental
dimensions still remain unexplored and have increasingly hindered responsible
AI. In this paper, we leverage the spatial flexibility of AI inference
workloads and propose equitable geographical load balancing (GLB) to fairly
balance AI's regional social and environmental costs. Concretely, to penalize
the disproportionately high social and environmental costs for equity, we
introduce $L_q$ norms as novel regularization terms into the optimization
objective for GLB decisions. Our empirical results based on real-world AI
inference traces demonstrate that while the existing GLB algorithms result in
disproportionately large social and environmental costs in certain regions, our
proposed equitable GLB can fairly balance AI's negative social and
environmental costs across all the regions.
