The trendy topics of sustainability and artificial intelligence (AI) rarely appear together. Yet the emissions caused by artificial intelligence are growing continuously. The spread of AI in companies in particular plays a major role here, as the number of productive AI applications is growing very strongly. To ensure that the AI area of a company can also be designed with sustainability in mind right from the start, we have collected measures and recommendations for "green" artificial intelligence (Green AI) in our whitepaper.
In the whitepaper, we answer basic questions such as:
How is environmental sustainability of AI measurable?
What measures are available to developers?
How can responsible parties make the data science sector more sustainable?
Sustainability of Artificial Intelligence
The environmental sustainability of AI focuses on resource optimization. Efficient algorithms with low data requirements and fast execution times can reduce CO2 footprints. But other dimensions of sustainability in terms of ethical and social issues should also be incorporated into the design of the field of AI.
Measuring environmental sustainability
The first step in improving corporate sustainability is to capture it. In addition to the direct measurement of energy consumption or the calculation of emissions, there are some substitute metrics that are well or less well suited for an initial estimate. A recording of the costs incurred in the cloud is inaccurate due to price changes on the part of the provider. On the other hand, tracking the CPU or GPU time, meaning the actual execution time on the processor, achieves a good estimation.
Green AI - Sustainable Artificial Intelligence for corporations
Best Practices
As long as no standards are established with regard to a resource-saving design of AI in the company, the developers are individually responsible for this. The best practices in our white paper include some applicable measures for model selection, training and operation. Further ideas for organizational measures as well as measures regarding the design of infrastructures are also included.
Overcoming obstacles
If you want to reduce the footprint of your Data Science division, there are a few hurdles to overcome. First of all, transparency must be created about the emissions caused and the scales. A reference value helps you to continuously track the developments. The second step should be to establish a training program to support employees and create awareness for sustainability. In a further step, your employees should be relieved by automating the capturing of the footprint of the models, code reviews as well as centralizing the responsibility for the topic Green AI.
If you have any questions on this topic or need advice on the sustainable design of your Data Science area, please feel free to contact us at any time or make an appointment for a non-binding exchange. We are happy to be there for you!
