Greening AI Inference with Accuracy and Latency-aware User Incentives

Problem
This paper addresses the gap in the literature regarding the environmental sustainability of AI inference, specifically focusing on the carbon emissions associated with AI services. Despite the growing concern over the ecological impact of AI, there is a lack of frameworks that effectively balance user preferences for inference quality and latency with environmental considerations. The authors propose a novel approach to incentivize users based on their valuation of these parameters, while also accounting for the trade-offs between carbon emissions and quality of experience (QoE). This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce a framework that incorporates user incentives into AI inference systems, allowing for a two-tier service subscription model. Users can opt for a discounted service that entails reduced inference quality and increased latency in exchange for lower carbon emissions. The framework is designed to adapt to varying trade-offs based on the size and complexity of AI models and the resource allocation for inference requests. The authors do not disclose specific architectural details or training compute requirements, focusing instead on the incentive structure and its implications for service delivery during periods of high carbon intensity.

Results
The paper presents a theoretical analysis of the proposed incentive framework, demonstrating its potential effectiveness through simulations. While specific numerical results are not provided, the authors claim that their approach can lead to significant reductions in carbon emissions while maintaining acceptable levels of user satisfaction. The framework is compared against traditional AI inference models that do not consider environmental factors, suggesting that the proposed model can achieve a more sustainable balance between user QoE and carbon output. However, exact effect sizes and performance metrics against named baselines on established benchmarks are not detailed.

Limitations
The authors acknowledge several limitations, including the need for empirical validation of their framework in real-world scenarios. They also note that user behavior and preferences may vary widely, which could affect the effectiveness of the incentive model. Additionally, the framework’s reliance on user willingness to accept lower quality and latency for environmental benefits may not be universally applicable. The lack of specific architectural and computational details limits the reproducibility of the proposed method, and the absence of quantitative results against established benchmarks raises questions about its practical efficacy.

Why it matters
This work has significant implications for the design of AI systems in an era increasingly focused on sustainability. By integrating user incentives that prioritize environmental consciousness, the proposed framework could lead to more responsible AI deployment practices. It encourages the AI community to consider the ecological impact of inference processes, potentially influencing future research on energy-efficient algorithms and models. The approach may also inspire further studies on user behavior in relation to environmental factors, paving the way for more sustainable AI solutions.

Authors: Vasilios A. Siris, Adamantia Stamou, George D. Stamoulis, Konstantinos Varsos, Ramin Khalili
Source: arXiv:2605.27309
URL: https://arxiv.org/abs/2605.27309v1