arXiv:2605.27309v1 Announce Type: new Abstract: 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 compl
Amidst growing public and scientific concern over AI's environmental impact, research is intensifying to find practical solutions for sustainable AI development and operation.
This research provides a framework for integrating environmental sustainability with user experience in AI services, which is crucial for the long-term viability and public acceptance of widespread AI adoption.
The explicit consideration of carbon emissions alongside accuracy and latency in AI inference design introduces new incentives for users and developers, potentially shifting priorities towards greener AI solutions.
- · AI service providers innovating for efficiency
- · Users prioritizing sustainability
- · Renewable energy sectors
- · AI hardware manufacturers with low-power designs
- · AI service providers with energy-intensive models
- · Carbon-intensive energy providers
- · Regions without abundant clean energy
AI developers will begin to incorporate carbon emission metrics as a key performance indicator alongside traditional ones like accuracy and latency.
Market demand for 'green AI' solutions will increase, driving investment into energy-efficient algorithms and hardware.
National and international regulatory bodies may introduce carbon emission standards for AI services, influencing global AI development strategies.
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