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https://github.com/daviddao/green-ai

🌱 The Green AI Standard aims to develop a standard and raise awareness for best environmental practices in AI research and development
https://github.com/daviddao/green-ai

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🌱 The Green AI Standard aims to develop a standard and raise awareness for best environmental practices in AI research and development

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# Green Artificial Intelligence Standard

[![](https://tinyurl.com/greenai-pledge)](https://github.com/daviddao/green-ai)



The Green AI Standard aims to develop a standard and raise awareness for best environmental practices in AI research and development



## The climate issue in AI

Developing machine learning models is extremely costly for the environment ([Strubell *et al.* (2019)](https://arxiv.org/abs/1906.02243))

- Training [BERT](https://arxiv.org/abs/1810.04805) on a GPU is roughly equivalent to a trans-American flight (650kg CO2)

- One (512px) [BigGAN](https://arxiv.org/abs/1809.11096) experiment is equivalent to a trans-Atlantic roundtrip (~1 to 2t of CO2)

- Neural architecture search experiments for [Transformer](https://arxiv.org/abs/1706.03762) is emitting as much as 50 years of an average human life (~280t of CO2) 



Information and communications technology is on track to create 3.5% of global emissions by 2020–which is more than the aviation and shipping industries–and could hit 14% by 2040 ([Guardian (2018)](https://www.theguardian.com/environment/2017/dec/11/tsunami-of-data-could-consume-fifth-global-electricity-by-2025)). We need to take a stand now!



## Best practices in development



1. Report time to retrain machine learning models (e.g. GigaFLOPS till convergence, [Strubell *et al.* (2019)](https://arxiv.org/abs/1906.02243))

2. Report sensitivity of hyperparameters for machine learning models (e.g. variance with respect to Hyperparameters searched, [Strubell *et al.* (2019)](https://arxiv.org/abs/1906.02243))

3. Use more efficient alternatives to brute-force grid search for hyperparameter tuning (e.g. random or bayesian search, [Strubell *et al.* (2019)](https://arxiv.org/abs/1906.02243))



## Best practices in infrastructure



Minimize costs and carbon emissions by sharing local infrastructure instead of relying on on-demand cloud computing resources ([Strubell *et al.* (2019)](https://arxiv.org/abs/1906.02243))



## Best practices in deployment



The fossil fuel industry is responsible for most of the world's CO2 emission by a large margin. Artificial intelligence has been a driving force of optimizing gas and oil extraction processes. By following the Standard, we pledge to not make developed applications available for fossil fuel focused usage.



## Offset your resulting emissions



We recommend offsetting your emissions to certified carbon neutrality projects if possible.

Offsets can be calculated via [MyClimate](https://co2.myclimate.org/en/offset_further_emissions) and purchased here:



- [MyClimate](https://www.myclimate.org/)

- [Gold Standard](https://www.goldstandard.org/)

- [ActForest](http://actforest.glideapp.io)



You can also measure how much power your deep learning model has consumed via [Power Meter](https://autoai-incubator.github.io/powermeter/). Note that it only covers GPU consumption.



## Show your commitment with a badge on your repository



| **👇 The Pledge Badge** | **👇 The Carbon Neutral Badge** |

|-----------------|-----------------|

| [![](https://tinyurl.com/greenai-pledge)](https://github.com/daviddao/green-ai) | ![](https://tinyurl.com/greenai-neutral) |



**The pledge badge** shows your commitment to do the best to reduce the greenhouse gas emissions caused by your research by following the best practices developed by the Green AI Standard



**The Carbon Neutral Badge** shows that your greenhouse gas emissions caused by your code repository are offsetted. The badge should link to the offset certificate for verification



## Acknowledgement



The green ring is inspired by the [Climate Reality project](https://www.climaterealityproject.org/blog/why-does-al-gore-wear-green-ring-pin)