Why 'green' matters
Training and serving large AI models consumes energy. In operational food‑safety systems—where models run continuously across many sites—energy efficiency affects cost, carbon footprint and inclusivity. ‘Green AI’ emphasizes efficiency metrics (FLOPs, energy per inference), encouraging model choices that balance accuracy with resource use.
Design choices for green analytics
Options include model compression (pruning, quantization), efficient architectures (small‑footprint CNNs, distilled models), edge inference to reduce data transfer, and batch scheduling to exploit low‑carbon electricity windows. Reporting efficiency metrics alongside accuracy makes trade‑offs explicit and helps practitioners choose models suited to deployment constraints.
Measurement & reporting
Good practice is to report both performance (AUC, F1) and cost (inference latency, energy per query, FLOPs). This encourages research and procurement decisions that lower environmental impact without sacrificing public‑health outcomes.
Conclusion
Green AI is not an optional add‑on: for scalable food‑safety analytics it is central. By measuring and optimizing energy use, practitioners can widen access, reduce operating costs and support climate‑aligned food systems.
References
- Green AI position paper — Schwartz et al., ‘Green AI’, arXiv 2019. https://arxiv.org/abs/1907.10597
- Green AI discussion — Communications of the ACM ‘Green AI’. https://dl.acm.org/doi/10.1145/3381831
- Energy metrics for ML — SEMI / TUDublin slides on energy & policy. https://openai.com/blog/ai-and-compute/
