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The area surrounding Phoenix, Arizona is a a hub for data centers, which put strain on the already limited freshwater resources in the Arizona desert. These data centers also require large amounts of electricity. According to Axios Phoenix, Arizona data centers consumed nearly 7.5% of the state's electricity in 2023, but could drain as much as 16.5% in 2030 in the highest growth scenario.

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"In many cases, adverse environmental impacts of AI disproportionately burden communities and regions that are particularly vulnerable to the resulting environmental harms." (Harvard Business Review, 2024)

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Key facts

E-waste is the fastest growing solid waste stream in the world (1).

In 2019, an estimated 53.6 million tonnes of e-waste were produced globally, but only 17.4% was documented as formally collected and recycled (2).

Lead is one of the common substances released into the environment if e-waste is recycled, stored or dumped using inferior activities, such as open burning (3).

E-waste recycling activities may have several adverse impacts on human health. Children and pregnant women are particularly vulnerable.

ILO and WHO estimate that millions of women and child labourers working in the informal recycling sector around the world may be at risk of e-waste exposure (4).

Overview

Every year millions of electrical and electronic devices are discarded as products break or become obsolete and are thrown away. These discarded devices are considered e-waste and can become a threat to the environment and to human health if they are not treated, disposed of, and recycled appropriately. Common items in e-waste streams include computers, mobile phones, and large household appliances, as well as medical equipment. Every year, millions of tonnes of e-waste are recycled using environmentally unsound techniques and are likely stored in homes and warehouses, dumped, exported or recycled under inferior conditions. When e-waste is treated using inferior activities, it can release as many as 1000 different chemical substances into the environment, including harmful neurotoxicants such as lead (3). Pregnant women and children are particularly vulnerable due to their unique pathways of exposure and their developmental status. The International Labour Organization (ILO) estimates that 16.5 million children were working in the industrial sector in 2020, of which waste processing is a subsector (4).

Scope of the problem

Electronic waste (e-waste) is the fastest growing solid waste stream in the world, increasing 3 times faster than the world's population (1). Less than a quarter of e-waste produced globally in 2019 was known to be formally recycled; however, e-waste streams contain valuable and finite resources that can be reused if they are recycled appropriately. E-waste has therefore become an important income stream for individuals and even communities. However, people living in low- and middle-income (LMICs), particularly children, face the most significant risks from e-waste due to lack of appropriate regulations, recycling infrastructure and training. Despite international regulations targeting the control of the transport of e-waste from one country to another, the transboundary movement of e-waste to LMICs continues, frequently illegally. E-waste is considered hazardous wasteas it contains toxic materials or can produce toxic chemicals when treated inappropriately. Many of these toxic materials are known or suspected to cause harm to human health, and several are included in the 10 chemicals of public health concern, including dioxins, lead and mercury. Inferior recycling of e-waste is a threat to public health and safety.

Exposure to e-waste

Electrical and electronic items contain many different toxic substances. While users are unlikely to have contact with any of these substances when the items are in use, when they become waste, these toxicants can be released into the environment if the devices are managed using environmentally unsound practices and activities. Several unsound practices have been observed at e-waste sites including:

scavenging

dumping on land or in water bodies

landfilling along with regular waste

opening burning or heating

acid baths or acid leaching

stripping and shredding plastic coatings

manual disassembly of equipment.

These activities are considered hazardous to the environment and human health as they release toxic pollutants, contaminating the air, soil, dust, and water at recycling sites and in neighbouring communities. Burning or heating is considered one of the most hazardous activities due to the toxic fumes created. Once in the environment, toxic pollutants from e-waste or produced through unsound recycling activities can travel significant distances from the point of pollution, exposing people in faraway areas to health-damaging substances.

Children are the most vulnerable

A range of adverse health outcomes linked to e-waste recycling activities have been posed.

Children and pregnant women are especially vulnerable to the effects of hazardous pollutants from informal e-waste recycling activities. Children are often involved in waste picking and scavenging, burning discarded e-waste and the manual dismantlement of items into component parts. In some countries, children may serve as a source of cheap labour and their small hands give them an advantage in taking apart the smallest items. These activities directly expose children to injury and high levels of hazardous chemicals. Working as a waste picker is hazardous labour and is considered one of the worst forms of child labour by the ILO. In 2020, the ILO estimated that as many as 16.5 million children globally were working in the industrial sector, of which waste processing is a subsector (4). It is unknown how many child labourers participate in informal e-waste recycling.

E-waste exposure may be linked to the following health effects during pregnancy and in infants and children:

adverse neonatal outcomes, including increased rates of stillbirth and premature birth;

neurodevelopment, learning and behaviour outcomes, especially associated with lead released through informal e-waste recycling activities; and

reduced lung and respiratory function and increased asthma incidence, which may be linked to high levels of contaminated air pollution that characterize many e-waste recycling sites.

Children and pregnant women are at higher risk than adults to contaminants released through informal e-waste recycling activities due to their unique vulnerabilities. Children have different exposuresto e-waste recycling activities. E-waste recycling activities release toxic chemicals that can cross the placenta and may contaminate breastmilk, for example mercury. Additionally, children are highly sensitive to many of the pollutants released through e-waste recycling due to their rapidly developing bodies, including their respiratory, immune and central nervous systems. E-waste contains multiple known and suspected neurotoxicants, including lead and mercury, that may disrupt the development of the central nervous system during pregnancy, infancy, childhood and adolescence. Some harmful toxicants from e-waste may also impact the structural development and function of the lungs. Changes to children's developing systems from e-waste may cause irreparable harm and affect them for the rest of their lives.

Prevention and management

National and international actions are essential to protect communities from dangerous e-waste recycling activities. Actions that can be taken include:

adopting and enforcing high-level international agreements;

developing and implementing national e-waste management legislation that protects public health;

incorporating health protection measures into national legislation;

monitoring e-waste sites and surrounding communities;

implementing and monitoring interventions that improve informal e-waste recycling activities, protect public health and ensure vital sources of community revenue;

educating health workers across all levels on e-waste-related child health issues;

eliminating child labour.

International agreements

The Basel Convention controls the transboundary movement of hazardous wastes and their disposal. It is a comprehensive environmental agreement that aims to tackle issues surrounding hazardous wastes, including e-waste and its management. In 2019, the Ban Amendment to the Basel Convention entered into force. It prohibits the movement of hazardous wastes, including e-waste, from countries of the Organisation for Economic Co-operation and Development (OECD), the European Commission countries and Liechtenstein to other states that are party to the Convention. The Basel Convention runs programmes and workshops to develop and deliver guidance on environmentally sound management of e-waste. It also provides states with guidelines to distinguish between waste and non-waste and the transboundary movement of e-waste. Additionally, regional conventions also exist, such as the Bamako Convention and the Waigani Convention. Both these regional conventions have arisen in response to the Basel Convention and aim to further restrict the movement of hazardous wastes, including e-waste, in African and South Pacific countries, respectively.

WHO response

WHO's Initiative on E-waste and Child Health is contributing to a number of international e-waste programmes and pilot projects in countries in Latin America and Africa. These pilot projects are developing frameworks to protect children's health from e-waste exposures that can be adapted and replicated in other countries and settings. The Initiative aims to:

increase access to evidence, knowledge and awareness of the health impacts of e-waste

improve health sector capacity to manage and prevent risks

facilitate monitoring of exposure to e-waste and interventions that protect public health.

In 2021, WHO released its first global report on e-waste and child health, which called for greater effective and binding action to protect children from the growing threat. WHO has developed training tools for the health sector, such as the recently updated training package for health care providers, including a specific training module on lead and on e-waste and child health. Additionally, WHO contributes to multi-agency capacity training tools including a MOOC and a joint course with PAHO.
References

1. Tackling informality in e-waste management: the potential of cooperative enterprises. Geneva: International Labour Organization; 2014 (https://www.ilo.org/sector/Resources/publications/WCMS_315228/lang--en/index.htm)

2. Forti V, Balde CP, Kuehr R, Bel G. The Global E-waste Monitor 2020: quantities, flows and the circular economy potential. Bonn, Geneva, Vienna: United Nations University, International Telecommunication Union, International Solid Waste Association; 2020 (https://ewastemonitor.info/)

3. Widmer R, Oswald-Krapf H, Sinha-Khetriwal D, Schnellmann M, Böni H. Global perspectives on e-waste. Environ Impact Assess Rev. 2005;25(5):436-458.

4. Child labour: global estimates 2020, trends and the road forward. Geneva: International Labour Organization; 2021 (https://www.ilo.org/ipec/ChildlabourstatisticsSIMPOC/lang--en/index.htm).

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WHO fact sheet on e-waste, including key facts, impacts on children, prevention and WHO's response.

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AI Environmental Impact Primer from Hugging Face

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AI Environmental Impact Primer from Hugging Face

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Hugging Face is an open source data science and machine learning platform. They are an AI company that undertook the tech sector's first attempt to estimate the broader carbon footprint of a large language model. Check out their primer to dive deeper into learning about the environmental impacts of AI.

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By: Sasha Luccioni, Bruna Trevelin, Margaret Mitchell (Hugging Face)

Image source: https://betterimagesofai.org/

Artificial intelligence (AI) has an environmental cost. Beginning with the extraction of raw materials and the manufacturing of AI infrastructure, and culminating in real-time interactions with users, every aspect of the AI lifecycle consumes natural resources - energy, water, and minerals - and releases greenhouse gases. The amount of energy needed to power AI now outpaces what renewable energy sources can provide, and the rapidly increasing usage of AI portends significant environmental consequences. The goal of this primer is to shed light on the environmental impacts of the full AI lifecycle, describing which kinds of impacts are at play when, and why they matter.

While some research and documentation on AI's environmental impacts currently exists, the nature and extent of AI's effects are under-documented, ranging from its embodied and enabled emissions to rebound effects due to its increased usage. Regulatory and policy initiatives, both existing and in progress, have the challenge of encouraging innovation and growth while addressing environmental impacts and how they affect different stakeholders. Ways forward range from technical interventions to make AI models more efficient, to policy interventions to incentivize sustainable AI research and practice.

Download the PDF version of the Primer here!

Recent years have ushered in a new era of growth and adoption of Artificial Intelligence (AI) technologies, especially generative AI, which is increasingly used in tools and systems spanning from Web search to customer service. While this era brings with it potential gains in terms of profit and productivity, its impact on already strained natural resources cannot be overlooked. The goal of the present primer is to outline fundamental pieces underpinning AI's direct impacts on the environment. It is written to be legible for the general public, policymakers, and the AI community. As such, it is organized to detail which natural resources are used throughout the AI lifecycle, and what the effects are on the environment for each.

Initial work on the environmental impacts of AI models focused on estimating the CO ₂ emissions incurred during model training. This includes the seminal work of Strubell et al., which calculated that training a large language model (LLM) with 213 million parameters was responsible for 626,155 pounds of CO ₂ emissions, roughly equivalent to the lifetime emissions of five cars, including fuel. Follow-up studies have looked at other types of model architectures, their energy use and emissions, confirming the consequential environmental impact of AI training.

Luccioni et al. proposed to extend these analyses to consider other stages of the AI model lifecycle, including training, deployment, and the components that make these possible: material extraction, equipment manufacturing, and overhead energy usage (cooling, networking, storage), among others (see Fig 1 below). Their work demonstrated that estimates of AI's greenhouse gas emissions are more than double than previous studies that focused on training in isolation, revealing a fuller picture of AI's environmental impact.

Fig 1. The life cycle assessment approach proposed by Luccioni et al.

Luccioni et al. also provided the first study on the environmental impact of AI model deployment) by analyzing the energy usage and carbon emissions of running the BLOOM large language model (LLM) in the cloud - specifically, a Google Cloud Compute cloud instance that contained the 176 Billion parameter BLOOM model, which received 230,768 queries over a period of 18 days. They found that this deployment used an average of 40.32 kWh of energy per day (roughly the equivalent of 1,110 smartphone charges) and, given the energy mix used by the computing instance, emitted approximately 19 kgs (42 lbs) of CO 2eq per day, reflective of state-of-the-art AI model deployment in 2023. This study was followed up by Luccioni et al. in 2024, which looked at the variation in energy usage and carbon emissions across different types of AI tasks. They found distinct differences based on modality (text vs. image) as well as whether the model was creating new content (captions, summaries, etc., commonly referred as "generative AI") or returning existing content. They found that image-based tasks and those that create new content used more energy.

It can be hard to understand the extent of AI's impacts on the environment given the separation between where you interact with an AI system, and how that interaction has come to be - most AI models run on data centers that are physically located far away from their users, who only interact with their outputs. But the reality is that AI's impressive capabilities come with a substantial cost in terms of natural resources, including energy, water and minerals, and non-negligible quantities of greenhouse gas emissions.

Every time we query an AI model - be it via our phone, a chat interface, or a smart speaker - this request has to run through physical hardware to provide us with an answer. As AI models get bigger in size and complexity, they also require more powerful hardware to run them. One common hardware component for AI systems is the GPU (graphical processing unit), and an individual AI model may require multiple GPUs. These GPUs are often on servers within data centers located across different regions of the world and connected via the Web. On average, 40-50% of the energy used by a data center is used for powering the computing equipment, with a further 30%-40% dedicated to cooling the equipment. Energy is also used for manufacturing the hardware (e.g., GPUs), as well as other elements of data center infrastructure (e.g., storage, networking, etc.) - although the exact amount used by companies such as Nvidia, who have considerable market shares on GPU design, remains unknown.

There is currently little transparency on the energy demands of specific AI applications, although a recent estimate has put the amount of energy used for a ChatGPT query to be anywhere between 6-10 times more than a traditional Web search (0.3 Wh vs. 2.9 Wh) [6] . At a macro level, AI is estimated to use 10%-20% of data center electricity today, but as new generations of AI-enabled servers consume more power, this percentage is set to increase at an average of 70% in coming years and double by 2030 .

Data centers currently account for 2-3% of the total electricity use in the United States, and this number is set to triple in the next 5 years . While new sources of renewable energy, such as solar and wind energy, are projected to meet approximately 40% of this demand, the rest is set to be met with non-renewable energy sources such as coal and natural gas. This would result in as much additional greenhouse gas as 16 million gas-powered cars . The combined energy use of the main technology companies providing AI cloud computing services and products (such as Google, Microsoft, Meta, and Amazon) has more than doubled in the last five years, and these companies are also among the largest purchasers of corporate renewable energy in the United States, having purchased almost 50 GW of renewable energy to date, as much as the generation capacity of Sweden . These same companies have also put out ambitious net-zero goals but have recently announced that they are failing to meet them due in part to the energy demands of AI tools and services . Overall, the growing energy demand for AI is significantly outpacing the increase in renewable energies - entailing substantial new GHG emissions and squeezing an already tight renewable energy market.

Just as the components in your personal computer heat up as they're used, so too do the components in computer servers. Data centers used for AI systems house hundreds of thousands of these servers, carrying out intensive, round-the-clock computation. They therefore need constant cooling to avoid overheating. One of the key ways in which this is done is by pumping clean water through radiators in the data center, which absorbs the heat in the air. The water used for cooling data centers cannot be saltwater or greywater because it clog or corrode cooling systems. This water then has to be cooled down; a significant portion of it evaporates in the process . After cooling, the remaining water is then reused or cleaned and cooled before being discharged back into aquifers.

Generally speaking, the amount of water used for data center cooling differs depending on data center configuration, but can range from 0.18 to 1.1L of water per kWh of energy . There are no official figures for specific AI models, but third-party research has estimated that GPT-3, the AI model underpinning the popular ChatGPT, uses 500mL of water for every 10 to 50 queries, although this number would depend highly on where it is deployed and how efficient the data center is. An average hyperscale data center, such as those used for training AI models, uses around 550,000 gallons (2.1 million liters) of water daily . Notably, some of them are built in areas with little water supply, such as the Arizona desert, where electricity is available, but water is scarce.

Computing chips such as GPUs are built on a thin layer of semiconductor, usually silicon, upon which components made out of different types of metals such as aluminum, copper, tin, tantalum, lithium, gallium, germanium, palladium, cobalt and tungsten are added. Mining of these metals also comes with environmental costs, since hundreds of tonnes of ore typically need to be dug up and processed to get a single ton of relatively common metals such as copper or aluminum. Minerals such as cobalt and tungsten are considered to be 'conflict minerals', meaning that they are mined or traded in areas of conflict, and contribute towards perpetuating human rights abuses and armed conflict.

The usage of water, minerals, and energy throughout the AI lifecycle often comes with emissions of greenhouse gasses - such as carbon dioxide. One major source is from the production of concrete and steel used in data centers; worldwide, concrete production is estimated to generate up to 8  percent of all human CO₂ emissions, in addition to using water and minerals. Another primary source of greenhouse gas emissions is from generating the electricity needed in different stages of the AI lifecycle. In Luccioni et al.'s 2023 study of the BLOOM language model, they found that of the total 50 tonnes of CO2eq emissions emitted during model training, only half was due to the energy consumption of the GPUs used for training BLOOM ('dynamic consumption'), with a further 29% stemming from the idle consumption of the data center (i.e., the energy used for heating/cooling, networking, storage, etc.), and a final 22% of emissions were produced from the GPU manufacturing process. The carbon intensity of the grid used is therefore the main factor influencing the final quantity of emissions incurred by AI models, impacting both model training and deployment.
Table 1: Breakdown of CO₂ emissions for the BLOOM model (Adapted from Luccioni et al., 2023)
For a full picture of AI's environmental impact, we need both consensus on what to consider as part of "AI", and much more transparency and disclosures from the companies involved in creating it. AI refers to a broad set of techniques, including machine learning, but also rule-based systems. A common point of contention is the scoping of what constitutes AI and what to include when estimating its environmental impacts. Core to this challenge is the fact that AI is often a part of, as opposed to the entirety of, any given system - e.g. smart devices, autonomous vehicles, recommender systems, Web search, etc. How to delineate and quantify the environmental impacts of AI as a field is therefore a topic of much debate, and there is currently no agreed-upon definition of the scope of AI. Some notable areas lacking coherent information include:

The embodied emissions of hardware manufacturing: While there are some numbers about the embodied (i.e., supply chain) emissions of different types of computing hardware, there are currently no reliable numbers on the carbon cost of manufacturing GPUs, which are the most popular type of hardware used for training AI models. Gathering these numbers for different generations and types of GPUs is important to have a better understanding of AI's lifecycle.

The embodied carbon of AI infrastructure: A significant contributing factor in the carbon emissions of data centers is the use of concrete in their construction. While initiatives have been aimed at providing sustainable building materials for data center construction, the relative impact of concrete construction on AI emissions is unclear.

Rebound effects and unintended consequences: It has been observed in different sectors (such as transportation) that an increase in efficiency in resource use will generate an increase in resource consumption rather than a decrease. While there is currently emphasis placed on making both AI hardware and models more efficient, the rebound effect that this may have (e.g. by increasing overall resource use) is still unclear and needs further quantification and longitudinal tracking.

AI's enabled emissions: AI technologies are increasingly used in sectors such as oil and gas exploration, improving their efficiency and increasing yield, which results in increased emissions overall . The proportion of these emissions that can be attributed to AI is still unclear, but initial work is being done to better understand this topic.

Ecosystem harm: Industrial waste produced by AI structures such as data centers risk environmental contamination . Further, the construction of new buildings - data centers as well as buildings where AI development occurs - risks negative effects on local ecosystems and existing natural habitats.

Different regulations may deal with multiple elements employed in the AI supply chain (e.g. regulations regarding mining, water, labor, hardware, software, data), but more specific regulation addressing AI and the environment is emerging. As discussed throughout this primer, the discussion of what constitutes AI is open and the same goes to how AI and the environment should be dealt with by regulation. Below is a non-exhaustive list of initiatives regarding AI and environmental challenges.

The EU launched the European Green Deal, a set of proposals to make the EU fit for reducing net greenhouse gas emissions, and to which EU's actions and policies have to contribute. As part of the Green Deal, the EU Climate Law has set legally binding targets for carbon neutrality by 2050, and AI systems used in energy management, smart grids, and environmental monitoring will need to comply with this law. A related initiative is the delegated regulation to establish an EU-wide scheme to rate the sustainability of EU data centers, as part of the new Energy Efficiency Directive. Water consumption could be addressed and regulated, for data centers built in the EU, through a thorough application of the Water Framework Directive.

The text of the Act also encourages the adoption of voluntary codes of conduct that would include measures to evaluate and minimize the environmental impact of AI systems. However, given that the adoption of codes of conduct is non-binding, it is unclear how effective it will be in achieving the stated objectives.

Spain: Sustainability was a key part of Spain's recently announced National AI strategy , with a specific emphasis on investing in computationally efficient models and data centers powered by renewable energy sources.

Canada: Environmental sustainability is one of the key focuses of the Pan-Canadian Artificial Intelligence Strategy. According to this strategy, by 2030 Canada intends to have a robust national AI ecosystem founded on scientific excellence, training and talent pools, public-private collaboration and advancing AI technologies to bring positive social, economic and environmental benefits for people and the planet. However, Canada's Artificial Intelligence and Data Act does not have specific provisions regarding the environmental impacts of AI.

France: The AI and Green transition roadmap, part of the broader France Nation Verte ecological planning program, highlights the potential of data, and more specifically AI, to meet the five major challenges of the ecological transition: consumption of resources, preserving biodiversity, mitigating and adapting to global warming and reducing pollution that impacts health.

International instruments: The OECD Recommendation of the Council on Artificial Intelligence has as one of its key principles the pursuit of inclusive growth, well-being, sustainable development and environmental sustainability with AI. The Hiroshima Process International Guiding Principles for Organizations Developing Advanced AI Systems proposes a non-exhaustive list of guiding principles, including prioritizing the development of AI systems to address the world's greatest challenges, notably but not limited to the climate crisis. The United Nations Global Digital Compact includes a commitment to promote environmental sustainability across the life cycle of digital technologies and aim to ensure that digital infrastructure and equipment are sustainably designed for the mitigation of and adaptation to climate change.

There is no single, one-size-fits-all approach to reducing the environmental impacts of AI models, but a variety of technical, behavioral and organizational interventions can be adopted by different stakeholders, at different stages of the AI lifecycle:

Technical interventions: different approaches are possible to make AI models less resource-intensive. This includes techniques such as pruning, quantization, distillation, and flash attention. These approaches can be applied at various stages of development, both during model training to make it more compute-friendly as well as on already-trained models to make them more efficient.

Behavioral interventions: users of AI models can also contribute to minimizing AI's environmental impacts by choosing task-specific (as opposed to multi-task) models when possible. AI model developers can benchmark the energy consumption of different models and opt for the most efficient ones, for instance, based on metrics such as the AI Energy Star project, and adopting strategies such as flexible scheduling for training AI models, which would allow for the optimization of energy sources.

Organizational interventions: institutions can also implement best practices with potentially far-reaching impacts in terms of the environment. This can include opting for compute instances powered by renewable energy sources when possible, measuring the energy consumption and greenhouse gas emissions of AI-enabled products and providing these metrics to users of these products.

Policy interventions: enforce transparency for user-facing AI systems, and transparency in general regarding the environmental impact of AI systems, start regulating different systems based on how often they're used, promote standards and incentives for meeting these standards (such as tax rebates), boost innovation through funding, incentivize sustainable applications and market opportunities and prioritize the most impactful solutions, including based on informed analysis of policy efforts.

@inproceedings{ai_environment_primer, author = {Sasha Luccioni and Bruna Trevelin and Margaret Mitchell}, title = {The Environmental Impacts of AI -- Policy Primer}, booktitle = {Hugging Face Blog}, year = {2024}, url = {https://doi.org/10.57967/hf/3004}, doi = {10.57967/hf/3004} }

AI model deployment: The process of using an AI model to carry out the task that it was trained for - e.g. generating images, finding answers to questions, etc.

AI model training: The process of providing data to an AI model that is meant to enable it to produce more accurate predictions.

: Since different greenhouse gases that are generated during electricity generation have different global warming potentials, these are commonly reduced to a common denominator, that of carbon dioxide, in order to make comparisons easier.

Embodied emissions: Emissions associated with the whole lifecycle of an item, including production of materials, construction, transportation, and item usage.

Generative tasks: Tasks that require a model to generate new outputs based on inputs - e.g., generating a poem based on an image, or generating an image based on a text prompt.

Graphical Processing Unit (GPU): A type of computing hardware originally designed to accelerate the rendering of graphics in video games given their parallel processing capabilities. They are now the most commonly-used type of hardware for training and deploying AI models.

Greenhouse gases (GHGs): gases that exist in the Earth's atmosphere that trap heat within it, thereby contributing towards the greenhouse effect (i.e. raising the surface temperature), e.g. carbon dioxide, methane, nitrous oxide.

Large language model (LLM): There is no single, agreed-upon definition of large language models (also called 'frontier models' or 'foundation models'), but they are largely defined as computational models that are capable of taking natural language as input or producing it as output, sometimes alongside other modalities such as images. See Rogers and Luccioni (2024) for a more in-depth discussion.

Model architectures: Depending on the task at hand and the amount of data available, there are different architectures of AI models that can be used - ranging from simpler ones such as decision trees to more complex ones such as transformers and convolutional neural networks. See Goodfellow et al (2016) for more details.

Thank you to Brigitte Tousignant for her help in editing this primer, and Philipp Hacker, Yacine Jernite, Lynn Kaack and David Rolnick for their invaluable comments and suggestions.

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The Environmental Impacts of AI -- Primer

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A Blog post by Sasha Luccioni on Hugging Face

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Nuclear Energy and Artificial Intelligence 

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Nuclear Energy and Artificial Intelligence 

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The growing energy demands of AI technologies are prompting tech companies to consider nuclear power as a low-emission solution. Experts at UBC's School of Public Policy and Global Affairs (SPPGA) caution against overreliance on nuclear energy due to economic, environmental and waste management concerns. They advocate for prioritizing proven renewable energy sources over risky nuclear power options and for fostering a more critical public debate on AI's unchecked growth.

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School of Public Policy and Global Affairs

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Nuclear Energy and Power-Hungry AI: Faculty Expert Opinion | School of Public Policy and Global Affairs

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With the power-hungry tech industry looking to nuclear energy to meet the rising demands of Artificial Intelligence (AI), SPPGA Director Allison Macfarlane and Prof. M. V. Ramana provide expert opinion about its risks, costs, and long-term sustainability. AI’s Energy Demands and Nuclear’s Uncertain Future In the recent Georgetown Journal of International Affairs article, “AI’s Energy […]