Greening AI Inference with Accuracy and Latency-aware User Incentives
It addresses the environmental sustainability of AI inference for AI service providers by introducing a practical incentive mechanism.
This paper proposes a framework for designing user incentives that reduce carbon emissions from AI inference by offering discounts for lower quality and higher latency, accounting for user valuation and environmental consciousness.
The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework for designing AI inference incentives based on the users' valuation for inference quality and latency, together with their environmental consciousness, while accounting for the tradeoff between carbon emissions and the two QoE parameters. Our approach can accommodate different tradeoffs, that depend on the size and complexity of the AI models and the allocation of resources to serve inference requests. The incentives can be offered through a practical two-tier service subscription that offers users a discount in exchange for reduced carbon emissions. The discounted service option gives the AI provider the flexibility to serve some percentage of inference requests at a lower quality and higher latency during periods of high carbon intensity.