Towards Environmentally Equitable AI

Nonetheless, the technological advancement of AI relies on computationally intensive calculations and thus has led to a surge in resource usage and energy consumption. Even putting aside the environmental toll of server manufacturing and supply chains, AI systems can create a huge environmental cost to communities and regions where they are deployed, including air/thermal pollution due to fossil fuel-based electricity generation and further stressed water resources due to AI's staggering water footprint [12, 25]. To make AI more environmentally friendly and ensure that its overall impacts on climate change are positive, recent studies have pursued multi-faceted approaches, including efficient training and inference [5], energy-efficient GPU and accelerator designs [19], carbon forecasting[14], carbon-aware task scheduling[1, 21], green cloud infrastructures[2], sustainable AI policies [10, 18], and more. Additionally, data center operators have also increasingly adopted carbon-free energy(such as solar and wind power) and climate-conscious cooling systems, lowering carbon footprint and direct water consumption [8]. Although these initiatives are encouraging, unfortunately, a worrisome outcome-- environmental inequity -- has emerged [3]. That is, minimizing the total environmental cost of a globally deployed AI system across multiple regions does not necessarily mean that each region is treated equitably. In fact, the environmental cost of AI is often disproportionately higher in certain disadvantaged regions than in others. Even worse, AI's environmental inequity can be amplified by existing environmental equity agnostic resource allocation, load balancing, and scheduling algorithms and compounded by enduring socioeconomic disparities between regions.