The challenges and applications of AI (and the long road to renewables)
In this Artificial Footprint series, we have explored the impacts of training Artificial Intelligence (AI) models, the use of AI and the emissions that go along with this. Now let’s take a look at some of the challenges in AI, how we use it and what the journey looks like to renewables.
Obscurity Through Complexity: The opacity issue
One challenge for anyone seeking to understand the carbon emissions impact of AI programs is the lack of readily available, precise information on the computing resources and hardware that went into these programs.
This opacity is partially due to a deliberate lack of transparency on the part of companies that make AI and other ICT technologies. In most cases, there is no incentive for companies to be upfront about their compute and energy use. If their energy use is seen as something that may put customers off, they may even be incentivised to hide it to protect profits.
The lack of clarity is also due to the inherent complexity of the supply chains that underpin AI and ICT products. This has been explored by studies such as Graham and Haarstad, pointing out that as commodities become more globalised, the production processes are broken into a complex set of supply chains and networks with geographically diverse nodes. Such complexity obscures the impacts of products from customers and makes it difficult even for customer watchdog organisations to perform analysis on commodity chains.
Crawford and Joler also highlight this as an issue, particularly in metal supply chains, which they describe as a “zooming fractal of tens of thousands of suppliers, millions of km of shipped materials and hundreds of thousands of workers.”
This complexity means that it’s not only costly and time-consuming for a company to attempt to ensure they are ethically sourcing materials - it can also make it incredibly difficult. Global ICT company Intel ran into this problem in 2009 when they set out to ensure that all their supplies of minerals such as tantalum, tin, tungsten, gold and cobalt were sourced from ‘conflict-free’ sources. It wasn’t until 2013, four years later, that Intel finally understood its own suppliers and supply chains well enough to certify that all minerals used in their products came from ‘conflict-free’ sources.
That four year timeframe was for a company that was actively trying to understand and improve its supply chain from within. How much harder and more time-consuming would it be for external organisations, researchers, journalists or consumers to untangle these supply chains and understand what goes into products such as AI, especially in cases where companies are deliberately exhibiting a lack of transparency?
Cloud computing providers are particularly bad at providing carbon transparency. Whilst most now publish some metrics, the numbers they provide are often difficult to compare across providers and are impossible to verify. They also typically only consider the carbon emissions from a subset of sources such as energy use, whereas correctly accounting for environmental sustainability requires considering a broader range of issues. Initiatives such as the Circular Data Centre Compass aim to remedy this, but more engagement from cloud providers is required.
In order to truly move towards environmentally sustainable AI, there will need to be far greater levels of openness and transparency from AI and ICT companies, sparked by either demand from consumers or - more likely - by legislation and enforcement.
How We Use It: Applications of AI
When it comes to AI inference and its potential impact on the environment, emissions from operational costs are not the whole story. It is also important to consider the ways in which AI technology can be utilised to directly help or harm the environment. Even if an AI is carbon-neutral, the ways in which it is being used may not be environmentally responsible.
How can we use AI to benefit the environment? Research has shown that AI can be used to save energy by improving the efficiency of systems, enabling them to derive more output from fewer resources. For example, testing by Google indicates that turning cooling control in their data centres over to AI could reduce energy use by 40%, whilst the IEA finds that digital technologies (including AI) could save energy in transport and buildings.
AI can also help the environment in other ways. For example, the World Bee Project is utilising AI to help reverse the decline in the bee population.
On the other hand, AI has the potential to be used with environmentally devastating consequences. Studies have shown that using AI to boost production could result in unsustainable consumption of resources, and could result in biodiversity loss if used to maximise agricultural yields without taking negative externalities into account. AI could also be used to increase the production of oil and gas, and the amount of recoverable reserves, which would conflict with vital efforts to phase out fossil fuels needed to avert severe climate change, as highlighted at the recent CoP 28.
The potential for AI to harm the environment suggests that policy change will be needed to prevent it, but so far this hasn’t been widely reflected in AI law. The EU’s proposed AI act, for example, has several policies to minimise the use of AI in socially harmful ways or in ways that impinge on personal privacy and freedoms, but almost nothing to prevent AI being used in ways that impact the environment - despite one of their listed priorities being to ensure that AI used in the EU is environmentally friendly. This is an oversight that other jurisdictions seeking to enact AI laws can rectify.
At the government, organisational and individual level, it is important to consider how AI could be used to help and harm the environment, and to bring both aspects into the wider cultural debate around ethical use of AI.
No Silver Bullets: AI and renewables
AI’s emissions come primarily through electricity use. If a gradual global shift towards renewable energy means that AI’s power demand could one day be entirely met by renewables, is it worth worrying about AI’s carbon footprint today?
Whilst we could (and should) meet electricity demand from renewables as soon as we can, it does not change the fact that currently there is still a significant amount of carbon in the electricity mix, and thus AI emissions will remain high in the short term. Working towards carbon-neutral AI does not excuse lack of action in dealing with today’s problems.
Firstly, decarbonising the grid requires big investments in renewable capacity in order to replace fossil-fuel-based generation. This becomes significantly more difficult if overall demand continues to increase. China, for example, is a world leader in installed renewable capacity, yet its emissions have continued to grow almost every year as demand continues to increase due to a growing economy and a more affluent populace. Growing demand makes it harder to decarbonise. It’s crucial, therefore, to promote demand-side reduction in order to facilitate supply-side decarbonisation, and this should be strongly considered as part of the debate around AI.
Secondly, the move towards renewables has its own problems with decarbonisation. Manufacturing of batteries and solar cells consumes significant electricity and hence has an associated emissions cost whilst the grid still uses non-renewable sources. These devices also typically consume materials like lithium, the mining of which has negative local environmental effects and direct emissions as mentioned above. So even if AI training and use consumed only energy from renewable sources, there would still be significant carbon emissions in the supply chain to consider.
So how can we do better as individuals, governments and organisations? In the next article, we propose six key policy recommendations to promote environmentally friendly use of AI.
This article is part of the Artificial Footprints Series, taken from our report by Owain Jones:
Tags: