Kate Crawford, researcher, author, and leading AI scholar, refers to Artificial Intelligence as an "extractive" technology. In her book Atlas of AI she compares the AI industry to mining, drawing comparisons between oil and precious metal extraction.

Extractive AI

The use of rare earth minerals and metals in the manufacturing of electronic components is a crucial aspect of the development of AI. These materials are used in the production of components such as batteries, memory chips, and processors. Lithium, for example, is a key component in the production of batteries used in devices such as smartphones, laptops, and electric cars. Similarly, cobalt is a vital component of rechargeable batteries, while copper is essential for wiring and other electrical components.

However, the extraction and refining of these materials are resource-intensive processes that have significant environmental impacts. The mining of rare earth minerals and metals can result in soil erosion, deforestation, and water pollution. It can also lead to the displacement of local communities and the destruction of their habitats. The production of electronic components also generates a significant amount of greenhouse gas emissions, contributing to climate change.

The demand for these materials is expected to increase dramatically as AI technologies continue to develop and become more widespread. This increase in demand will only exacerbate the environmental impact of their extraction and use.

The Hidden Costs of the Cloud

Cloud computing relies on massive data centres and infrastructure that consume a significant amount of energy and produce waste. These data centres require constant cooling, lighting, and other support systems. The construction and operation of data centres also require huge amounts of energy, water, and other resources, leading to carbon emissions and other forms of environmental damage.

One of the most significant environmental impacts of cloud computing is its contribution to climate change. According to one estimate, the carbon footprint of the IT industry is 1.8% to 3.9% of global greenhouse-gas emissions.

Many companies have pledged to make their data centres carbon-neutral or powered by renewable energy sources, but critics argue that these efforts are not enough. Offsetting carbon emissions or engaging in carbon trading does not address the underlying problem of energy consumption and waste production.

Case Study: The Carbon Cost of Training Large Language Models

Large language models are powerful AI systems that can generate natural language texts for various applications. Unfortunately, training these models requires a huge amount of computational resources and energy, which has a significant environmental impact.

According to a study by researchers at the University of Massachusetts Amherst, training a single LLM can emit as much carbon as five cars in their lifetimes. The study estimated the energy consumption and carbon footprint of four popular LLMs: Transformer, ELMo, BERT, and GPT-2. The results showed that the most energy-intensive model, Transformer, consumed 656,347 kWh of electricity and emitted 626,155kg of CO2 equivalent. This is equivalent to "nearly five times the lifetime emissions of the average American car".

Organisations like Microsoft, OpenAI, and Google are investigating ways to reduce this impact, including choosing more efficient models, using pre-trained models or transfer learning, reducing the frequency of training sessions, using renewable energy sources, and implementing best practices for data collection and processing.

Teaching AI Ethics

  • Legal Studies: What laws and regulations exist to govern the use of AI in environmental contexts? How can they be improved to better protect the environment?
  • Environmental Science: How can AI be used to address environmental challenges such as climate change, pollution, and deforestation? What ethical considerations should be taken into account?
  • Mathematics: How can algorithms and machine learning be used to model and predict the impact of human activities on the environment?
  • Social Studies: How does environmental degradation disproportionately affect different communities, particularly marginalised and vulnerable groups?
  • Engineering: How can engineering principles be used to design environmentally sustainable AI systems?
  • Business and Economics: How can AI be used to promote sustainable business practices and reduce the environmental impact of industries?
  • Health and PE: How can AI be used to address public health issues related to the environment, such as air and water pollution?
  • English and Literature: How can we use literary criticism like ecocriticism to analyse representations of AI in literature and popular culture?