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The Promise Of AI: Can It Hold For Environmental Sustainability? – Analysis


We need to be in a better position to do our homework and genuinely assess the environmental impact of digital technologies, including AI.

By Cathleen Berger

We are in a climate crisis and we are living through a global pandemic that is severely affecting economic stability. Aware of the risk, that government incentives to boost growth post-pandemic may well undermine many of the necessary investments and reductions to mitigate the climate crisis, we increasingly hear calls for a “green recovery.” Thereby, AI is often presented as a powerful solution to fuel this green transition. But is that true?

Different implementations of AI may certainly provide opportunities for change, including when it comes to advancements in medicine, food production, traffic management, and more. At the same time, any implementation of AI builds on massive and still growing volumes of data that need be stored and processed, which has a significant environmental impact. In addition to mitigating harmful uses of AI that amplify discrimination and bias, undermine privacy, and violate trust online, we need a lot more transparency around its environmental impact, too. This boils down to two initial key elements: Familiarising ourselves with the environmental costs of AI and introducing mandatory, transparent emissions reporting into AI regulation.

What do we know about AI’s environmental impact?

Illustrative research from Massachusetts Institute of Technology (MIT) showed that training popular natural language processing AI models produced the same CO2 as flying roughly 300 times between Munich, Germany and Accra, Ghana. One of these models is called GPT-2, which was estimated to require 284 mtCO2e.

In June 2020, GPT-3 was released — a model that is exponentially bigger than its predecessor. GPT-3 builds on 175 billion parameters, whereas the 2019 GPT-2 model builds on “only” 1.5 billion parameters.

In any case, there are countless models and implementations with similar or even bigger scope and larger data sets that all add to the overall environmental impact of AI.

And even just this one model consuming 284 mtCO2e could instead power 33 US homes for an entire year.

In addition, we have to account for the physical presence of data centres which occupy extensive surfaces of land and put significant strain on global water resources, factors that are not consistently reflected in corporate sustainability reports.

Greenhouse gas emissions (GHG) assessments

Greenhouse gas emissions accounting is incredibly complex. Although it forms the basis upon which we can identify where and how to improve, it is surprising how little detail or meaningful guidance there is about how to measure the environmental impact of digital products like AI.

Companies only rarely publish the information necessary to make such calculations and if they share findings or results of their assessments, methodologies remain vague.

Most companies report on the basis of the GHG Protocol, yet there are considerable differences in the (public) accounting of emissions. Some only report against scope 1 and 2 (“operational emissions”), others include scope 3 value chain emissions, like business travel, events, or purchased goods and services, but share little about materiality or methodologies.

The difficulty of clear boundaries was also visible in Mozilla’s 2019 GHG report, in which the use of its products, like Firefox, contributed roughly 98 percent of the organisation’s overall emissions. However, whether people read the news, use their email client, watch cat videos, or shop was not distinguished, instead the assessment accounts for overall time spent online. While insightful for the internet’s impact at large, it will be challenging to mitigate the impact of its digital products specifically on such rough estimates.

What do we need going forward?

The question is not, whether technology has a role to play to fuel both a green recovery and long-term societal transformation, but which technologies will make a net-positive difference. To answer that, we need to be in a better position to do our homework and genuinely assess the environmental impact of digital technologies, including AI. Otherwise, any claim that AI supports a green transition will remain unsubstantiated as environmental costs are not properly accounted for.

To put it more bluntly: Positive uses of AI for mitigating the climate crisis can only be net-positive if we know what their own environmental impact is, including training, storing, processing of data, and the physical presence of data centres.

Standards and mandatory, transparent reporting

Systemic challenges require nuanced solutions. To innovate sustainable, we must ensure that we have the details we need to make informed decisions.

This start, we need better standards for GHG accounting and mandatory, transparent reporting against all scopes and categories of the GHG protocol.

We need regulation for environmental impact assessments in the tech sector, including for digital products like AI. This also means investing in open sourcing emission factors, calculation formulas and tools that do not just approximate but help calculate and determine the impact of digital products, too.

Ultimately, I like to think that in the discussions around privacy and data protection, we have grown more willing to stop and ask: Is everything we can do, really what we should do? This is exactly the mindset we now need with a view to the environmental impact of AI as well: Does the benefit of the suggested solution really outweigh its negative environmental impact?

Only then will we be able to really fuel a green recovery and healthy societal transformation to mitigate both, the effects of the pandemic and the climate crisis.

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