Five ways to develop sovereign, sustainable AI solutions

Now that organisations understand AI and what it can achieve, businesses around the world are focusing on how to build it responsibly. Three of the five main themes at the Paris AI Action Summit examine the need for responsible AI, with separate streams on trust, public interest and good governance.

These themes are not simple. In addition to the core function of AI tools – for example, considering what an AI app does, how it does it, and whether bias is present – most businesses are starting to realise that they need to consider the deeper ‘AI supply chain’.

This is not just altruistic. A number of LLM tools are currently facing the risk of lawsuits for copyright infringement, because they may have been trained without due content permission. AI tools that present biased results are quickly exposed in press, leading to reputational damage and a loss of customer trust. Some countries also have legislation permitting data usage for economic intelligence purposes – but in another region, this may represent a data breach. AI has also received negative publicity for ‘running hot’ and consuming large amounts of energy and water in datacenters.

However, AI can also be a tremendous force for good – if handled correctly. So, what should businesses be thinking about so that they get the most from AI, without incurring undue commercial or reputational risk?

1- Consider Sovereignty from the Start

Understand your data ‘supply chain’ from the very beginning of the process. For example, if you’re using an external LLM for a chatbot, where was this developed? Which data was it trained on, and was this data acquired ethically?

“AI can often be a black box when it comes to processing data,” says Lex Avstreikh, Strategy Lead for Stockholm-based AI firm Hopsworks. “It’s far too complex to show how the system arrived at any one decision. But if you can show people the inputs and the outputs, then that goes a long way to building transparency and trust.”

2- Plan for a Sovereign Future

It’s important to think about where data will be during its future lifecycle – will you be running in an external datacenter, and where will data be in transit and at rest? Where are the headquarters of the datacenter company in question and what does this mean from a regulatory and handling perspective? Perhaps most importantly, will your customers be happy with all of these arrangements?

This was the decision journey faced by Swedish AI firm Ebbot. In July 2020, the Data Protection Commission v. Facebook Ireland case, commonly referred to as Schrems II, resulted in the Court of Justice of the European Union (CJEU) issuing a decision that added more regulations to data protection and processing principles. Ebbot recognised the importance of data security and compliance and thus made it a priority to store and process all data within the EU.

3- Location, location, location

Location isn’t just an important sovereignty concern – it’s also crucial to sustainability. Although Scandinavia may have very green energy, it’s easy to forget that many cloud providers will offer geographical ‘computing zones’ rather than defined locations, which can result in a less green footprint. CPU- and GPU-intensive tasks like model training should be run in green energy zones wherever possible, and are rarely latency-dependent; consequently, you can locate them far away if necessary.

When your AI app goes into production, also remember that backup and redundancy are a necessity – but will also increase your carbon footprint. Consider having a ‘low power’ or passive backup if commercially feasible – it will take longer to bring online in the case of emergency, but you’ll be consuming less power.

4- Always Consider Necessity

A lot of organisations only consider hardware efficiency and power consumption during the development process, but green software is rapidly gaining popularity. Having efficient code which is still fit for purpose can have a huge impact on power consumption, particularly if you’re building an app for very broad use. “We’ll definitely see more efficient and specific LLMs, because they’re absolutely needed,” added Avstreikh.

Although organisations are often considering the cost of development, with FinOps initiatives, we are also seeing the dawn of GreenOps, ensuring that technology is as green as possible from end to end. To that effect, consider benchmarking the CPU and memory usage of your application, because less hardware-intensive apps are usually less power-hungry.

5- Re-use, recycle

Developing bespoke code can make sure that it’s as lean and efficient as possible, but it can also use needless computing power to develop. Many technology organisations will offer PaaS offerings that can automate common parts of the application development and deployment process. For example, consider our AI Endpoints solution, which helps developers to access other AI models, from Bert to Mistral to Llama, all using a simple API.

This is not an easy process, but establishing responsible AI conduct in your organisation’s DNA will avoid complications further down the road, and also show to customers that you are considering data – including theirs – in a responsible, secure way. With increasing numbers of organisations tracking not only their scope three emissions, but also their data supply chains in a more comprehensive fashion, sovereignty and sustainability are two clear ‘musts’ for any modern AI company.

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If you’re a startup or scale-up building an AI solution, and would like to work with a sovereign, sustainable cloud provider in turn, you can find more information about OVHcloud – including our cloud credit scheme – on our startup hub.