这篇论文为 AI 服务提供商提供了一个实用方案，通过用户激励平衡碳排放与服务质量，关注绿色 AI 的团队可以直接参考其两级订阅设计。
AI 服务的广泛使用引发了环境可持续性担忧，其中 AI 推理的碳排放是主要贡献者。本文提出一个框架，基于用户对推理质量和延迟的估值以及环保意识，设计激励措施，同时权衡碳排放与这两个 QoE 参数。该框架可适应不同 AI 模型规模和资源分配下的权衡。激励通过实用的两级服务订阅实现，用户以折扣换取碳排放减少。在碳强度高时，折扣服务允许 AI 提供商以较低质量和较高延迟服务部分推理请求。
Greening AI Inference with Accuracy and Latency-aware User Incentives
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.